Assessment of the functional movement screen and injuries in gymnasts
Bibliographic record
Abstract
Objective: To identify possible differences in movement quality through the functional movement screen (FMS) between injured and non-injured adolescent acrobatic gymnasts in the last season. Method: descriptive, comparative, cross-sectional study involving 20 adolescent female gymnasts divided into two groups, one composed of 9 gymnasts who had suffered an injury in the last season (14,7±1,56) and the other composed of 11 gymnasts who had not suffered any injury (13,9±2,25). The FMS battery was used, consisting of seven tests: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight leg raise, trunk stability in push-ups, trunk rotational stability. Results: Of the nine gymnasts who had sustained an injury, 66.6% were located in the lower limb, ankles and knees. The results of the total functional assessment of FMS using the Mann Whitney U statistic for independent samples showed no statistically significant differences between groups (Z = -.393; p > 0.05), with the average range of FMS being similar in both cases (10.05 and 11.06 in injured and non-injured gymnasts respectively). It also showed the absence of significant differences in each of the tests of the battery, and no relationship was found through Spearman’s R statistic, between the overall FMS score and the group of injured gymnasts. Conclusion: The results of the FMS total score were slightly higher in gymnasts who were not injured last season, as well as slightly better in all the lower body tests, hence the FMS can be used as a preventive programmed to detect possible deficiencies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".