FMS Scores Change With Performersʼ Knowledge of the Grading Criteria—Are General Whole-Body Movement Screens Capturing “Dysfunction”?
Bibliographic record
Abstract
Deficits in joint mobility and stability could certainly impact individuals' Functional Movement Screen (FMS) scores; however, it is also plausible that the movement patterns observed are influenced by the performers' knowledge of the grading criteria. Twenty-one firefighters volunteered to participate, and their FMS scores were graded before and immediately after receiving knowledge of the movement patterns required to achieve a perfect score on the FMS. Standardized verbal instructions were used to administer both screens, and the participants were not provided with any coaching or feedback. Time-synchronized sagittal and frontal plane videos were used to grade the FMS. The firefighters significantly (p < 0.001) improved their FMS scores from 14.1 (1.8) to 16.7 (1.9) when provided with knowledge pertaining to the specific grading criteria. Significant improvements (p < 0.05) were also noted in the deep squat (1.4 [0.7]-2.0 [0.6]), hurdle step (2.1 [0.4]-2.4 [0.5]), in-line lunge (2.1 [0.4]-2.7 [0.5]), and shoulder mobility (1.8 [0.8]-2.4 [0.7]) tests. Because a knowledge of a task's grading criteria can alter a general whole-body movement screen score, FMS or otherwise, observed changes may not solely reflect "dysfunction." The instant that individuals are provided with coaching and feedback regarding their performance on a particular task, the task may lose its utility to evaluate the transfer of training or predict musculoskeletal injury risk.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".