FMS Scores Change With Performersʼ Knowledge of the Grading Criteria—Are General Whole-Body Movement Screens Capturing “Dysfunction”?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it