An Evaluation of the Apprehension, Relocation, and Surprise Tests for Anterior Shoulder Instability
Bibliographic record
Abstract
BACKGROUND: Although there are many studies describing tests for shoulder instability, there are few assessing the validity of these tests in diagnosing anterior shoulder instability. PURPOSE: To assess the validity of the apprehension, relocation, and surprise tests as predictors of anterior shoulder instability. STUDY DESIGN: Retrospective review of prospectively collected data. METHODS: Forty-six patients with a clear diagnosis of one of the following shoulder disorders were evaluated by four independent, blinded examiners: traumatic anterior instability (18), rotator cuff tendinosis (17), posterior instability (2), glenohumeral osteoarthritis (4), or multidirectional instability (5). Interobserver reliability was also determined. RESULTS: In subjects who had a feeling of apprehension on all three tests, the mean positive and negative predictive values were 93.6% and 71.9%, respectively. The surprise test was the single most accurate test (sensitivity = 63.89%; specificity = 98.91%). An improvement in the feeling of apprehension or pain with the relocation test added little to the value of the tests. Interobserver reliability was determined to be 0.83. CONCLUSIONS: and CLINICAL RELEVANCE: The results of this study suggest that a positive instability exam on all three tests is highly specific and predictive of traumatic anterior glenohumeral instability.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".