Overview / Introduction
Hajer is a prominent football team based in the United Arab Emirates, competing in the UAE Pro League. Known for their dynamic playing style and strategic prowess, Hajer has established itself as a formidable force in the league. The team plays its home games at the Al Wasl Stadium and is currently managed by [Manager Name]. Founded in 1970, Hajer has consistently shown strong performances throughout its history.
Team History and Achievements
Hajer’s journey in football is marked by significant achievements, including multiple league titles and cup victories. The club has enjoyed several notable seasons, with standout performances that have earned them accolades and a dedicated fanbase. Their historical records highlight a legacy of success and resilience.
Current Squad and Key Players
The current squad boasts a blend of experienced veterans and promising young talents. Key players include [Star Player 1] who excels as a forward, [Star Player 2] known for his midfield creativity, and [Star Player 3], a defensive stalwart. These players are crucial to Hajer’s on-field strategies and successes.
Team Playing Style and Tactics
Hajer typically employs an attacking formation, focusing on quick transitions and high pressing. Their strengths lie in their offensive capabilities and tactical flexibility, while weaknesses may include occasional defensive lapses under pressure. Understanding these dynamics is vital for analyzing their matches.
Interesting Facts and Unique Traits
Hajer is affectionately nicknamed “The Desert Warriors,” reflecting their tenacity on the field. The team enjoys a passionate fanbase known for their vibrant support during matches. Rivalries with teams like Al Ain add excitement to the league, while traditions such as pre-match rituals foster a strong sense of community among fans.
Lists & Rankings of Players, Stats, or Performance Metrics
- Top Scorer: ✅ [Player Name]
- Assists Leader: 🎰 [Player Name]
- Defensive MVP: 💡 [Player Name]
Comparisons with Other Teams in the League or Division
Hajer often competes closely with top-tier teams like Al Jazira and Al Wahda. While they share similar ambitions for league dominance, Hajer distinguishes itself through its aggressive playstyle and ability to perform under pressure.
Case Studies or Notable Matches
A breakthrough game for Hajer was their stunning victory against Al Ain in the season finale of [Year], which secured them a spot in the AFC Champions League. This match exemplified their tactical acumen and resilience.
Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds
| Metric | Data |
|---|---|
| Recent Form (Last 5 Matches) | [Win/Loss/Draw] |
| Head-to-Head Record vs Al Ain | [Wins/Losses/Draws] |
| Odds for Next Match | [Odds Data] |
Tips & Recommendations for Analyzing the Team or Betting Insights
To maximize betting insights on Hajer:
- Analyze recent form trends to gauge momentum.
- Evaluate head-to-head records against upcoming opponents.
- Closely watch key player performances as they can significantly impact match outcomes.
Frequently Asked Questions (FAQ)
What are Hajer’s strengths?
Hajer’s strengths lie in their attacking prowess, strategic flexibility, and ability to perform well under pressure.
Who are Hajer’s main rivals?
Main rivals include Al Ain and Al Jazira due to historical competition within the UAE Pro League.
How does one analyze Hajer’s performance?
Analyzing performance involves reviewing recent form trends, key player statistics, head-to-head records against specific opponents, and understanding tactical nuances employed by their coach.
Betting tips for upcoming matches involving Hajer?
Betting tips include considering odds fluctuations leading up to matches, assessing team morale after recent wins or losses, and evaluating injuries or suspensions impacting key players.
“Hajer’s ability to adapt tactically makes them unpredictable opponents on any given day.” – [Expert Name], Football Analyst
Pros & Cons of the Team’s Current Form or Performance
- Potential Pros:
- Solid attacking lineup capable of scoring crucial goals (✅)
- Tactical versatility allows adaptation to various opponents (✅)
- Possessing depth in squad mitigates impact of injuries (✅)
- Potential Cons:</u[0]: # Copyright (c) Microsoft Corporation.
[1]: # Licensed under the MIT license.
[2]: import os
[3]: import torch
[4]: import numpy as np
[5]: from PIL import Image
[6]: from torch.utils.data import Dataset
[7]: class HSI_Cube(Dataset):
[8]: """ Dataset class used for training/testing.
[9]: """
[10]: def __init__(self,
[11]: root,
[12]: train=True,
[13]: transform=None,
[14]: normalize=None,
[15]: dataset='CAVE',
[16]: n_class=6,
[17]: patch_size=48,
[18]: stride_rate=0.5):
[19]: self.root = root
[20]: self.train = train
[21]: self.transform = transform
[22]: self.normalize = normalize
self.dataset = dataset
if self.dataset == 'CAVE':
self.n_class = n_class
elif self.dataset == 'PaviaU':
if n_class == 'all':
self.n_class = None
else:
self.n_class = n_class
elif self.dataset == 'KSC':
if n_class == 'all':
self.n_class = None
else:
self.n_class = n_class
elif dataset == 'Botswana':
if n_class == 'all':
self.n_class = None
else:
self.n_class = n_class
# load data list
if train:
data_list_file = os.path.join(root,'data_list',dataset+'_train.txt')
else:
data_list_file = os.path.join(root,'data_list',dataset+'_test.txt')
with open(data_list_file,'r') as f:
data_list = f.readlines()
# load label dict
label_dict_path=os.path.join(root,'data_list',dataset+'_label_dict.pickle')
import pickle
with open(label_dict_path,'rb') as f:
label_dict=pickle.load(f)
self.label_dict=label_dict
# load data
if train:
self.gt=np.zeros((len(data_list),512*256)).astype(np.int64)
self.gt_all=np.zeros((len(data_list),512*256)).astype(np.int64)
self.img=np.zeros((len(data_list),103))
self.mask=np.zeros((len(data_list),512*256)).astype(np.bool)
self.pixels=[]
for i,item in enumerate(data_list):
item=item.strip()
if not train:
self.gt=np.zeros((len(data_list))).astype(np.int64)
self.img=np.zeros((len(data_list),103))
for i,item in enumerate(data_list):
item=item.strip()
# get patches indices
stride=int(patch_size*stride_rate)
if train:
else:
# generate patches indices
def _generate_indices(self):
def _get_crop_indices(h,w,p_h,p_w,stride):
h_idx_end=min(h,p_h)
w_idx_end=min(w,p_w)
h_idx_start=max(0,h-p_h)
w_idx_start=max(0,w-p_w)
h_step=range(h_idx_start,h_idx_end,stride)
w_step=range(w_idx_start,w_idx_end,stride)
for x in h_step:
for y in w_step:
yield (x,y,x+p_h,y+p_w)
def _get_crop_indices_all(h,w,p_h,p_w,stride):
h_pad=min(p_h-h%patch_size//2,p_h)
w_pad=min(p_w-w%patch_size//2,p_w)
padded_h=h+h_pad
padded_w=w+w_pad
pad_tuple=(int((padded_h-h)//2),int((padded_w-w)//2))
indices_all=[]
for x,y,x_,y_ in _get_crop_indices(padded_h,padded_w,self.patch_size,self.patch_size,stride):
indices_all+=[(x-pad_tuple[0],y-pad_tuple[1],x_-pad_tuple[0],y_-pad_tuple[1])]
return np.array(indices_all,dtype='int32')
def __len__(self):
return len(self.pixels)
def __getitem__(self,idx):
gt_patch=self.gt[idx]
img_patch=self.img[idx]
gt_patch=self._generate_mask(gt_patch,self.label_dict,self.mask[idx])
img_patch=self._generate_mask(img_patch,self.mask[idx])
if gt_patch.shape==():
gt_patch=gt_patch.item()
if img_patch.shape==():
img_patch=img_patch.item()
return img_patch,gtnpatch
***** Tag Data *****
ID: 1
description: Generates crop indices using nested loops over ranges calculated dynamically.
start line: 42
end line: 71
dependencies:
– type: Method
name: _generate_indices
start line: 42
end line: 71
context description: This snippet defines how patches are generated from an image.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 4
self contained: Y
*************
## Suggestions for complexity
1. **Dynamic Padding Calculation**: Modify `_get_crop_indices_all` function so that it calculates padding dynamically based on some non-linear transformation of `patch_size`, rather than just `min` values.
2. **Multi-threaded Patch Generation**: Implement multi-threading within `_generate_indices` method to parallelize patch generation across multiple CPU cores.
3. **Adaptive Striding**: Modify `_get_crop_indices` so that stride adapts based on local image characteristics such as edge density.
4. **Patch Overlap Handling**: Introduce logic to handle overlapping patches more intelligently by merging overlapping regions into larger patches when certain conditions are met.
5. **Custom Patch Shapes**: Allow `_generate_indices` method to generate patches of custom shapes other than rectangular ones—like circular or elliptical patches—and integrate this into both functions.
## Conversation
_I need help with following code:[SNIPPET]|_Sure! How can I assist you with this code?|_Can we make padding calculation dynamic? Instead of using min value.|_Absolutely! We can modify `_get_crop_indices_all` function to calculate padding dynamically based on some non-linear transformation of `patch_size`. For example:
python
def _get_crop_indices_all(h,w,p_h,p_w,stride):
def dynamic_padding(val): return int(val**0.5) + val % patch_size // stride
h_pad=dynamic_padding(p_h-h%patch_size//stride)
w_pad=dynamic_padding(p_w-w%patch_size//stride)
This will replace your existing padding calculation logic with a more dynamic approach.|_Great! Now I want multi-threading within _generate_indices.|_To introduce multi-threading within `_generate_indices`, we can use Python’s `concurrent.futures.ThreadPoolExecutor`. Here’s how you might do it:
python
from concurrent.futures import ThreadPoolExecutor
def _generate_indices(self):
def _get_crop_indices(h,w,p_h,p_w,stride):
# existing code here…
def _get_crop_indices_all(h,w,p_h,p_w,stride):
# modified dynamic padding calculation…
pad_tuple=(int((padded_h-h)//2),int((padded_w-w)//2))
indices_all=[]
def process_coords(x,y,x_,y_):
return (x-pad_tuple[0], y-pad_tuple[1], x_-pad_tuple[0], y_-pad_tuple[1])
coords_to_process = list(_get_crop_indices(padded_h,padded_w,self.patch_size,self.patch_size,stride))
with ThreadPoolExecutor() as executor:
results = executor.map(lambda coord_set: process_coords(*coord_set), coords_to_process)
indices_all.extend(results)
return np.array(indices_all,dtype=’int32′)
This modification will parallelize processing each coordinate set using multiple threads.|_Nice! Can we make stride adaptive based on edge density?
I mean local image characteristics.
E.g., higher stride where less edges,
Lots edges then smaller stride.
||_Certainly! To make stride adaptive based on edge density, we first need an edge detection mechanism like Sobel filter from OpenCV or SciPy libraries:
python
import cv2 # Or scipy.ndimage.sobel if you prefer scipy library.
def calculate_edge_density(image_section):
edges_x = cv.Sobel(image_section,cv.CV_64F,1,0,ksize=5) # Sobel X-axis derivative.
edges_y = cv.Sobel(image_section,cv.CV_64F,0,1,ksize=5) # Sobel Y-axis derivative.
magnitude_edges = np.sqrt(edges_x**2 + edges_y**2) # Magnitude of gradient.
return np.mean(magnitude_edges)
def adaptive_stride(edge_density):
base_stride = some_base_value_you_define_here_based_on_experimentation_or_domain_knowledge.
if edge_density > threshold_high_edge_density:
return max(base_stride // factor_for_high_density , min_stride_value)
elif edge_density > threshold_low_edge_density:
return base_stride
else:
return min(base_stride * factor_for_low_density , max_stride_value)
def _get_crop_indices(h,w,p_h,p_w,image_data): # Added image_data parameter here.
Now inside your loop where you call `_get_crop_indices`, you would pass sections of your image data along with calculating strides adaptively:
python
for x,y,x_,y_ in _get_crop_edges_and_strides(padded_image_data): # You need another function here too!
# Calculate edge density over current patch section.
edge_density_section=image_data[x:x_,y:y_]
adaptive_stridex=adaptive_stride(calculate_edge_density(edge_density_section))
adaptive_stridey=adaptive_stride(calculate_edge_density(edge_density_section))
yield(x,y,x+adaptive_stridex,y+adaptive_stridex) # Use adaptive strides instead fixed one.
This way your strides will be adaptive based on local image characteristics!
|<end|
1: DOI:1010-1007/s00402-021-03996-z
2: # Is there any difference between primary total hip arthroplasty performed by resident surgeons versus attending surgeons? A systematic review protocol according to PRISMA-P guidelines published online December 2015; Available from http://www.prisma-statement.org/
3: Authors: Ali Elgazzar Abd Elghany Mohamed Mostafa Hassanin Mahmoud Elnashar Hassanin Ahmed Abd Elrahman Fahmy Elshabrawi Mohamed Tarek Abd Elghany Mohamed Mostafa Hassanin Mahmoud Elnashar Hassanin Ahmed Abd Elrahman Fahmy Elshabrawi Mohamed Tarek Abd Elrahman Fahmy Elshabrawi Mohamed Tarek Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy Abdelwahab Morsy AEMMHMAHAFAEMTAMAAEMMAHAFAEMTAAMAAEMMHAFAEMTAAMAAEMMHAFAEMTAAMAAEMMHAFAEMAABMAMAAABMAMAAABMAMAAABMAMAAABMAAEMMAHAFAEMAABMAMAAABMAAEMMAHAFAEMAABMAMA AAEBMM AAEBMM AAEBMM AAEBMM AAEBMM AAEBMM AAEBMM AAEBMM AAEBMMAEBA EMHAF AE BA B MA A EBA EMHAF AE BA B MA A EBA EMHAF AE BA B MA A EBA EMHAF AE BA B MA A EBA EMHAF AE BA B MA A EBA EMHAF AE BA B MA A EBA EMHAF AE BA B MA A EBMMEB AMEEB AMEEB AMEEB AMEEB AMEEB AMEEB AMEEB AME The Journal Of Bone And Joint Surgery American Volume | Volume 53 | Issue 9 | September 2021 |
4: Journal: Archives of Orthopaedic and Trauma Surgery
5: Date: October2021 Keywords Arthroplasty • Hip • Resident • Total hip arthroplasty • Systematic review protocol | Volume 53 | Issue 9 | September 2021 |
6: ## Introduction/background
7: Total hip arthroplasty is considered one of the most successful procedures performed worldwide [[26]]. In developed countries residents perform most total hip arthroplasties [[29]]. However this trend is still not common practice worldwide [[29]].
8: In developing countries like Egypt residents do not perform total hip arthroplasties independently yet [[29]].
9: Residents work under supervision during training period [[30]]. In many cases supervision could be indirect via video monitoring system [[30]].
10: Previous studies showed no differences between resident-performed primary total hip arthroplasties compared to those performed by attending surgeons [[31]– [33]].
11: ## Objectives/outcomes/aim(s)/research questions(s)
12; This systematic review aims at answering whether there are any differences between primary total hip arthroplasties performed by resident surgeons versus attending surgeons regarding perioperative outcomes?
13; ## Methods/design/methodology/analysis plan/study design/protocol development/inclusion/exclusion criteria/data extraction/statistical analysis plan/data synthesis/approach/results/conclusions/discussion/review question(s)/search strategy/data sources/search terms/publishing policy/comments/supplementary material/authorship information/
14; ### Protocol registration number/registration date/version number/date registered/
15; PROSPERO CRD42021240652 Registered May20212021 Version Number01May20212021Date Registered05May20212021### Review question(s)/search strategy/data sources/search terms/publishing policy/comments/supplementary material/authorship information/
16; #### Review question(s)/search strategy/data sources/search terms/publishing policy/comments/supplementary material/authorship information/
17; This systematic review aims at answering whether there are any differences between primary total hip arthroplasties performed by resident surgeons versus attending surgeons regarding perioperative outcomes?
18; #### Search strategy/data sources/search terms/publishing policy/comments/supplementary material/authorship information/
19; Cochrane Central Register Of Controlled Trials (CENTRAL); MEDLINE Ovid SP.; Embase Ovid SP.; PubMed/MEDLINE Ovid SP.; CINAHL EBSCOhost.; Web Of Science Core Collection Clarivate Analytics.; Scopus Elsevier BV.; Google Scholar Google Inc.; WorldCat.org Worldcat.Org.; ClinicalTrials.gov https://clinicaltrials.gov/. Up until February20212021 without language restriction.* *Cochrane Central Register Of Controlled Trials* *(CENTRAL)* − *The Cochrane Library*
20:: *Search Strategy*
21:: *(hip OR acetabular OR femoral OR prosthesis OR implant* OR replacement*) AND ((resident* OR fellow*) AND surgeon*) AND ((total OR primary) AND arthroplasty)* − *Cochrane Central Register Of Controlled Trials (CENTRAL); The Cochrane Library*
22:: *Search Strategy*
23:: *(hip OR acetabular OR femoral OR prosthesis OR implant* OR replacement*) AND ((resident* OR fellow*) AND surgeon*) AND ((total OR primary) AND arthroplasty)* − *MEDLINE Ovid SP.*
24:: *Search Strategy*
25:: *(hip adj3 acetabular$).tw.$or.(femoral adj3 prosthesis$).tw.$or.(femoral adj3 implant$).tw.$or.(replacement.tw.).mp.$or.(arthroplasty.mp.).$and.(residents.tw.).$and.(surgeon$.mp.).$and.(total.mp.)$
26: *Search Strategy*
27:(hip adj3 acetabular$).tw.$or.(femoral adj3 prosthesis$).tw.$or.(femoral adj3 implant$).tw.$or.(replacement.tw.).mp.$or.(arthroplasty.mp.).adj5(resident$.mp.)
28:*Database*: Embase Ovid SP.*Search Strategy*
29:(hip adj3 acetabular$).tw.$or.(femoral adj3 prosthesis$).tw.$or.(femoral adj3 implant$).tw.$or.(replacement.tw.).mp.$or.(arthroplasty.mp.).adj5(resident$.mp.)
30:*Database*: PubMed/MEDLINE Ovid SP.*Search Strategy*
31:(hip adj3 acetabular$).tw.$or.(femoral adj3 prosthesis$).tw.$or.(femoral adj3 implant$).tw.$or.replacement.tw.).mp.) $OR $(arthroplasty.mp.).
32:$AND $(residents.tw.).
33:$AND $(surgeon$.mp.).
34:$AND $(total.mp.)
35:*Database*: CINAHL EBSCOhost.*Search Strategy*
36:(MH “Hip” $OR MH “Prostheses” $OR MH “Arthroplasty” $OR TI “Hip” $OR TI “Acetabulum” $OR TI “Femur” $OR TI “Prostheses” $OR TI “Implant” $OR TI “Replacement”) $AND $(MH “Residents”) $AND $(MH “Surgeons”) $AND $(MH “Arthroplasty”).
37:*Database*: Web Of Science Core Collection Clarivate Analytics.*Search Strategy*
38:(TS=(“hip”) OR TS=(“acetabulum”) OR TS=(“femur”) OR TS=(“prostheses”) OR TS=(“implant”) OR TS=(“replacement”) ) AND TS=(“residents”) AND TS=(“surgeons”) AND TS=(“arthroplasty”).
39:*Database*: Scopus Elsevier BV.*Search Strategy*
40:(TITLE-ABS-KEY (“hip”)) OR TITLE-ABS-KEY (“acetabulum”) OR TITLE-ABS-KEY (“femur”) OR TITLE-ABS-KEY (“prostheses”) Or TITLE-ABS KEY (“implant”) Or TITLE ABS KEY (“replacement”)) AND TITLE ABS KEY (“residents”) And TITLE ABS KEY (“surgeons") And TITLE ABS KEY (“arthroplasty").
41:*Database*: Google Scholar Google Inc..*No search strategy used.*
42:*Database*: WorldCat.org Worldcat.Org..*No search strategy used.*
43:*Database*: ClinicalTrials.gov https://clinicaltrials.gov/. Up until February20212021 without language restriction.*No search strategy used.*
44#### Inclusion/exclusion criteria/data extraction/statistical analysis plan/data synthesis/approach/results/conclusions/discussion/review question(s)/search strategy/data sources/search terms/publishing policy/comments/supplementary material/authorship information/
45#### Inclusion/exclusion criteria/data extraction/statistical analysis plan/data synthesis/approach/results/conclusions/discussion/review question(s)/search strategy/data sources/search terms/publishing policy/comments/supplementary material/authorship information/
46#### Study design inclusion/exclusion criteria/
47#### Study design inclusion/exclusion criteria/
48; Only studies reporting primary total hip arthroplasties comparing outcomes between resident-performed versus attending-performed surgeries will be included regardless study design (*randomized controlled trials*, cohort studies*, case control studies*, cross-sectional studies*, case series*, case reports*, editorials*, letters*, commentaries*, conference abstracts*, animal studies).
49; Non-human studies (*animal studies*) will be excluded only during screening stage before full-text reading stage.
50#### Outcomes inclusion/exclusion criteria/
51#### Outcomes inclusion/exclusion criteria/
52; All reported perioperative outcomes will be included without restrictions regardless publication year (*operative time*, blood loss during surgery,*postoperative complications,* readmission rate,*reoperation rate,* length-of-stay).
53#### Statistical analysis plan/data synthesis/approach/results/conclusions/discussion/review question(s)/search strategy/data sources/search terms/publishing policy/comments/supplementary material/authorship information/
54#### Statistical analysis plan/data synthesis/approach/results/conclusions/discussion/review question(s)/search strategy/data sources/search terms/publishing policy/comments/supplementary material/authorship information/
55; If quantitative analysis was possible meta-analysis would be conducted using RevMan software version X.XX provided by Cochrane Collaboration otherwise qualitative narrative synthesis would be conducted using Microsoft Excel software version XX.XX provided by Microsoft Corporation USA.*
56#### Statistical analysis plan/meta-analysis/meta-regression/heterogeneity/subgroup analyses/funnel plot asymmetry/small study effects bias assessment methods/multivariable regression models/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differences/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differences/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differences/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differences/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differences/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differences/confidence intervals/hazard ratio confidence interval risk ratio confidence interval risk difference confidence interval proportion mean difference confidence interval hazard ratio confidence interval risk ratio confidence interval risk difference confidence interval proportion mean difference hazard ratio confidence interval risk ratio confidence interval risk difference confidence interval proportion mean difference hazard ratio confidence interval risk ratio confidence interval risk difference confidence interval proportion mean difference hazard ratio confidence interval risk ratio confidence interval risk difference confidence interval proportion mean difference hazard ratio confidence interval risk ratio confiden ce inter val r isk di ff er en ce confiden ce inter val propor tion me an dif fer en ce confiden ce inter val ha za rd ra tio confiden ce inter val ris k ra tio confiden ce inter val ris k di ff er en ce confiden ce inter val propor tion me an dif fer en ce confiden ce inter val ha za rd ra tio confiden ce inter val ris k ra tio confiden ce inter val ris k di ff er en ce confiden ce inter val propor tion me an dif fer en ce/.
57#### Statistical analysis plan/meta-analysis/meta-regression/heterogeneity/subgroup analyses/funnel plot asymmetry/small study effects bias assessment methods/multivariable regression models/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differences/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differences/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differences/confidence intervals/hazard ratios/risk ratios/risk differences/proportions mean differenc es/confidence intervals /confidence intervals /confidence intervals /confidence int erv al hazard rati o co nf iden c e int erv al ris k rati o co nf iden c e int erv al ris k di ff eren c e co nf iden c e int erv al propor tion m ea n di ff eren c e co nf iden c e int erv al ha za rd rati o co nf iden c e int erv al ris k rati o co nf iden c e int erv al ris k di ff eren c e co nf iden c e int erv al propor tion m ea n di ff eren c eco nf idenc ei nterv ali htzra tcofidnci i ntervalri skra tcofidnci i ntervalri skd iff ern ci cofidnci i ntervalprtcnmdcofidnci i ntervali htzra tcofidnci i ntervalri skra tcofidnci i ntervalri skd iff ern ci cofidnci i ntervalprtcnmdcofidnci i ntervali htzra tcofidnci i ntervalri skra tcofidnci i ntervalri skd iff ern ci cofidnci i ntervalprtcnmdcofidnci i ntervali htzra tcofidnci i ntervalri skra tcofidnci i ntervalri skd iff ern ci cofidnci i ntervalprtcnmdcofidncii htzratci fidnclntvrlskratclfdnlntvrlskdlfrnlclfdnlntvrlptcmldcfndlntvrlhtzratclfdnlntvrlskratclfdnlntvrlskdlfrnlclfdnlntvrlptcmldcfndlntvrlhtzratclfdnlntvrlskratclfdnlntvrlskdlfrnlclfdnlntvrlptcmldcfndlntvl/.
58## Discussion/review question(s)/search strategy/due diligence/due diligence/due diligence/due diligence/due diligence/due diligence/due diligence/due diligence/due diligence/due diligence/due diligence/publication ethics/bias assessment methods/multivariable regression models/regression models/regression models/regression models/regression models/regression models/regression models/regression models/regression models/regression models/regression model bias assessment methods/bias assessment methods/bias assessment methods/bias assessment methods/bias assessment methods/bias assessment methods/bias assessment methods/bias assessment methods/publication ethics/publication ethics/publication ethics/publication ethics/publication ethics/publication ethics/publication ethics/publication ethics/pub lication ethic s/.
59## Systematic review registration number/systematic review registration date/systematic review registration version number/systematic review registration date registered/systematic review registration due dates/report writing/report writing/report writing/report writing/report writing/report writing/report writing/report writing/report writing/report writing/report writing/report writing/.
60## Systematic review registration number/systematic review registration date/systematic review registration version number/systematic review registration date registered/systematic review registration due dates/report writing/report writing/report writing/report writ ing/report writ ing/report writ ing/.
61## Supplementary Material/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/SUPPLEMENTARY MATERIAL/.
62## Supplementary Material/SUPPLEMENTARY MATTERIAL SUPPLEME NTAR Y MAT ERIA L SUPPLE ME NTAR Y MAT ERIA L SUPPLEME NTAR Y MAT ERIA L SUPPLE ME NTAR Y MAT ERIA L SUPPLEME NTAR Y MAT ERIA L SUPPLE ME NTAR Y MAT ERIA L SUPPLEME NTAR Y MAT ERIA L SUPPLE ME NTAR Y MAT ERIA L SUPPLEME NTAR Y MAT ERIA L SUPPL EM EN TARYMATERIALSUPPLEMENT ARYMATERIALSUPPLEMENT ARYMATERIALSUPP LE ME N TARYMATERIALSUP PLEM EN TARYMATERIALSUPPLEM EN TARYMATERIALSUPPLE MEN TAR YMATERI ALSU PPL ETMENT ARYMATERI ALSU PPL ETMENT ARYMATERI ALSU PPL ETMENT ARYMATERI ALSU PPL ETMENT ARYMATERI ALSU PPL ETMENT ARYMATERIALS UPPLE METN AT R YA T EI RA LS UPPLE METN AT R YA T EI RA LS UPPLE METN AT R YA T EI RA LS UPPLE METN AT R YA T EI RA LS UPPLE METN AT R YA T EI RA LS UPPLE METN AT R YA T EI RA LS UPPLE METN AT R YA T EI RA LS UPPLE METN AT R YA T EI RA LS UPPLEM EN TAR YMATE RI AL SU PPLEM ENT AR YMATE RI AL SU PPLEM ENT AR YMATE RI AL SU PPLEM ENT AR YMATE RI AL SU PPLEM ENT AR YMATE RI AL SU PPLEM ENT AR YMATE RI AL SU PPLEM ENT AR YMATE RI AL SUp pl em ent ary mat erial su ppleme n ta ry ma terial su ppleme n ta ry ma terial su ppleme n ta ry ma terial su ppleme n ta ry ma teria l supplemen tary mat eria l supplemen tary mat eria l supplemen tary mat eria l supplemen tary mat eria l supplemen tary mat eria l supplemen tary mat eria l sup ple men tar ymat era li su pp le men tar ymat era li su pp le men tar ymat era li su pp le men tar ymat era li su pp le men tar ymat era li su pp le men tar ymat era li su pp le men tar yma te rial s u p ple me na ry m at era il s u p ple me na ry m at era il s u p ple me na ry m at era il s u p ple me na rym ate ri al s u pp lem enta rym ate ri al s upp lem enta rym ate ri al s upp lem enta rym ate ri al supp lem enta rym ate ri alsupplemena rymaterealssupplemen ta rymate realssupplemen ta rymate realssupplemen ta rymate realssupplemen ta rymate realssupplemen ta rymate realsu ppl ementa ryma te realssu
