The Relationship between Training Load and Injury Risk in Basketball: A Systematic Review
Bibliographic record
Abstract
The relationship between training load and injury risk in basketball is an important area in sports injury prevention and performance enhancement; however, there is limited conclusive evidence of their associations. The aim of this systematic review was to examine the evidence of the relationship between training load and injury risk in basketball, which is one of the most common sports worldwide. This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive literature search was conducted on the PubMed, SCOPUS, and Web of Science databases up until March 2024. The search aimed to identify studies that prospectively and/or retrospectively examined the relationship between training load and injury risk in basketball. Inclusion criteria were limited to studies published before February 2024. The quality of each study was assessed using the Newcastle-Ottawa Quality Assessment Scale and Oxford Centre for Evidence-Based Medicine levels of evidence. A narrative synthesis of the findings was performed. A total of 14 articles met the inclusion criteria and were included in the review. Of these, 11 studies reported at least partially statistically significant results, providing evidence of a relationship between training load and injury risk. In conclusion, the findings of this review suggest a clear relationship between training load and injury risk in basketball.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".