Association between Body Weight and Body Mass Index and Patellar Tendinopathy in Elite Basketball and Volleyball Players, a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The features of Patellar-Tendinopathy are (1): pain localised to the inferior pole of the patellar; (2): the presence of load-related pain. Body-Weight and Body-Mass-Index, as two easily-measured variables, could potentially aid the prediction of PT. This review aims to establish relationships between Body-Weight and Body-Mass-Index and Patellar-Tendinopathy via synthesising the evidence from prospective-cohort and cross-sectional studies in elite basketball and volleyball players. Seven databases (PubMed, EMBASE, CINAHL, Google Scholar, Health-Management-Information-Consortium, National-Technical-Information-Service, ClinicalTrial.gov) and citation chasing were used to identify English peer-review articles from 2000 to 2022. An adapted version of the Newcastle-Ottawa scale was used for critical appraisal. Two reviewers were involved in literature searching, data extraction, and quality review. Two prospective cohort and five cross-sectional studies met the inclusion criteria, providing 849 subjects (male:female: 436:413). Five studies found BW is associated with PT. Three studies found a relationship between BMI and PT. Six out of seven studies were classified as very good studies. All studies were level IV evidence. The very low certainty evidence suggests an association between BW and PT. There is moderate certainty evidence that BMI is associated with PT. These preliminary findings should be treated cautiously due to the lack of strong evidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.020 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".