The supine moving apprehension test—Reliability and validity among healthy individuals and patients with anterior shoulder instability
Bibliographic record
Abstract
Background Performance-based tests for patients with anterior shoulder dislocation are lacking. This study determined the reliability and validity of the supine moving apprehension test designed to assess the ability to control anterior instability loads. Methods Thirty-six participants were recruited (18 healthy individuals, and 18 patients following anterior shoulder dislocation). Healthy participants performed the supine moving apprehension test on 2 separate occasions to determine test-retest reliability. Patients completed the supine moving apprehension test and the Western Ontario Shoulder Instability index before and 6 months after surgical stabilization of their shoulder. The presence of anterior apprehension was also documented post-operatively. Results The supine moving apprehension test demonstrated good test-retest reliability (intraclass correlation coefficient = 0.74−0.84). Patients performed 18−30 repetitions less than healthy individuals during the supine moving apprehension test ( P < 0.01). A strong correlation was found between supine moving apprehension test scores and Western Ontario Shoulder Instability post-operatively ( r = −0.74, P ≤ 0.01). Supine moving apprehension test scores significantly improved among patients following surgery ( P < 0.01). Patients with a negative apprehension test post-operatively performed the supine moving apprehension test significantly better than patients with a positive apprehension test ( P < 0.01). Conclusions The supine moving apprehension test is reliable and valid among patients with anterior shoulder dislocation and may serve to assess patients’ ability to control shoulder anterior instability loads.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".