CLINICAL RELIABILITY AND DIAGNOSTIC ACCURACY OF VISUAL SCAPULOHUMERAL MOVEMENT EVALUATION IN DETECTING PATIENTS WITH SHOULDER IMPAIRMENT.
Bibliographic record
Abstract
BACKGROUND: Clinical investigation of shoulder injuries commonly utilizes visual evaluation of scapular movement to determine if abnormal or asymmetrical movements are related to the injury. To date, the intrarater reliability and diagnostic accuracy of visual evaluation of scapular movement among physical therapists are not known. PURPOSE: The aims of this study were to determine the clinical reliability and diagnostic accuracy of physical therapists visual evaluation of scapulohumeral movements when used to diagnose shoulder impairment. STUDY DESIGN: University based laboratory and an internet based survey. METHODS: Thirty-three physical therapists and 12 patient participants participated in this study. Reliability was measured as percent agreement and using the free marginal kappa statistic (κ) and Cronbach's alpha (α) for interrater and intrarater reliability respectively. Diagnostic accuracy variables such as sensitivity, specificity, likelihood ratios were calculated from contingency table analysis. RESULTS: Visual evaluation yielded the following (95% CI): diagnostic accuracy 49.5%, specificity 60% (56 - 64), and sensitivity 35% (29 - 41), positive and negative likelihood ratios were 0.87 (0.66 - 1.14) and 1.09 (0.92 - 1.27) respectively. Percent agreements of evaluation findings between sessions for static and dynamic symmetry were 69% and 68%, respectively. The alpha statistics for static and dynamic symmetry were both 0.51. Percentage agreement in determining the injured shoulder was 59%, with an alpha statistic of 0.35. CONCLUSION: Visual evaluation of scapular movements, without additional clinical information, demonstrated a poor to fair reliability and poor to fair diagnostic accuracy. CLINICAL RELEVANCE: The clinical utility of the use of isolated visual scapular evaluation is cautioned. More reliable and valid objective measures are needed for diagnosing shoulder impairment. LEVEL OF EVIDENCE: 2b, Exploratory cohort study.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".