Performance in the Deep Squat Test and the risk of musculoskeletal injuries: a systematic review
Bibliographic record
Abstract
Deep Squat Test (DS) is a functional test frequently used in risk classification protocols for musculoskeletal injuries in physical activities. Objective: To systematically evaluate the literature on the validity of DS as a predictor of musculoskeletal injuries. Method: A search without language or time filters was carried out on the Medical Literature Analysis and Retrievel System Online (MEDLINE), Scientific Electronic Library Online (SciELO), Physiotherapy Evidence Database (PEDro) and Virtual Health Library (BVS) databases with the following title words: injury prediction, injury risk and deep squat. We included prospective studies of DS as a risk classification test for musculoskeletal injuries during the practice of physical activities until December 2016. The participants' profile, sample size, classification of musculoskeletal injuries, follow-up time, study design and results were extracted from the studies. The bias risk analysis was performed with the Newgate-Ottawa Scale. Results: Five studies were included, using different analyzes, whose results varied. The odds ratio ranged from 1.21 to 2.59 (95% CI = 1.01 - 3.28). The 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: DS is a test whose presence of movement dysfunctions is a predictor of the risk of musculoskeletal injuries in individuals who practice physical activities. However, due to the methodological limitations presented, caution is suggested when interpreting such results.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".