Movement Competency Screen: Rethinking the Rating
Bibliographic record
Abstract
Background Dancers are at high risk of musculoskeletal disorders. There has been a growing interest in the last few years in pre-season screening using tools to evaluate movement competency, among which is the Movement Competency Screen (MCS). It is currently scored using a categorical 3-level rating system, but this method does not seem to take into account the load level of movements. A 5-level scoring system could potentially alleviate this problem. Hypothesis/Purpose For each scoring system, to investigate (1) the internal consistency, and (2) the association with transversus abdominis activation (TrA), hip muscle strength and with Functional Movement screen (FMS TM ) total score. Study design Secondary analyses of a prospective cohort study. Methods One hundred and eighteen professional and preprofessional dancers evolving in ballet or contemporary dance were recruited. The MCS was performed and was scored according to the 3- and 5-level scoring systems. The key variables for movement competency that were considered for convergent validity were the activation ratio of the TrA evaluated with ultrasound imaging and hip strength assessed with a handheld dynamometer. Movement competency was also measured with the FMS TM . Results Internal consistency was higher for the 5-level scoring of the MCS items ( α =0.548) compared to the 3-level scoring system ( α =0.494). Multiple linear regressions showed that TrA activation, hip adductor strength, and FMS TM could significantly explain 24.0% of the variance for the 5-level scoring system of the MCS whereas hip internal rotator strength and FMS TM could explain only 16.4% of the variance for the 3-level scoring system. Conclusion The 5-level scoring system showed better metrologic properties in terms of internal consistency and concurrent validity and therefore, should be preferred over the 3-level scoring system in future research. Level of Evidence Level III
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".