Clinical Assessment of Physical Examination Maneuvers for Rotator Cuff Lesions
Bibliographic record
Abstract
BACKGROUND: Shoulder pain and disability pose a diagnostic challenge for clinicians owing to the numerous causes that exist. Unfortunately, the evidence in support of most clinical tests is weak or absent. PURPOSE: To determine the diagnostic validity of physical examination maneuvers for rotator cuff lesions. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 1. METHODS: Consecutive shoulder patients recruited for this study were referred to 2 tertiary orthopaedic clinics. A surgeon took a thorough history and indicated his or her certainty about each possible diagnosis. A clinician performed the physical examination for diagnoses where uncertainty remained. Arthroscopy was considered the reference standard for patients who underwent surgery, and MRI with arthrogram was considered the reference for patients who did not. The sensitivity, specificity, and likelihood ratios were calculated to investigate whether combinations of the top tests provided stronger predictions of the presence or absence of disease. RESULTS: There were 139 participants. None of the tests were highly sensitive for diagnosing rotator cuff tears or tendinosis. Tests for subscapularis tears were all highly specific. No optimal combination of tests improved the ability to correctly diagnose rotator cuff tears. Closer analysis revealed the internal rotation and lateral rotation lag sign did not improve the ability to diagnose subscapularis or supraspinatus tears, respectively, although the lateral rotation lag sign demonstrated a discriminatory ability for tear size. CONCLUSION: No test in isolation is sufficient to diagnose a patient with rotator cuff damage. A combination of tests improves the ability to diagnose damage to the rotator cuff. It is recommended that the internal rotation and lateral rotation lag signs be removed from the gamut of physical examination tests for supraspinatus and subscapularis tears.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".