Performance in the Deep Squat Test and musculoskeletal injuries: a systematic review
Bibliographic record
Abstract
Abstract Introduction: The Deep Squat Test has been applied in pre-season evaluations of sports teams and in military courses to predict the risk of musculoskeletal injuries. Objective: To evaluate the association of DS performance and the risk of musculoskeletal injuries. Methods: In this systematic review, a search without language or time filters was carried out in MEDLINE, SciELO, SCOPUS, SPORTDiscuss, CINAHL and BVS databases with the following title words: injury prediction, injury risk and deep squat in December 2016. Participants' profile, sample size, classification of musculoskeletal injuries, follow-up time, study design and results were extracted from the studies. Bias risk analysis was performed with the Newcastle-Ottawa Scale. Results: Five studies were included, using different analyzes, whose results varied. Odds ratio ranged from 1.21 to 2.59 (95% CI = 1.01 - 3.28); relative risk was 1.68 (95% CI = 1.50 - 1.87), sensitivity from 3 to 24%, specificity from 90 to 99%, PPV from 42 to 63%, NPV from 72 to 75% and AUC from 51 to 58%. Conclusion: The DS can be a test whose presence of movement dysfunctions is a predictor of the risk of musculoskeletal injuries in individuals who practice physical exercises. However, due to the methodological limitations presented, caution is suggested when interpreting such results. PROSPERO registration: CRD4201706922.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.007 | 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) | 0.006 | 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".