Diagnostic Accuracy of Clinical Tests in Detecting Rotator Cuff Pathology
Bibliographic record
Abstract
Purpose: There is minimal information on predictive value of strength-related clinical tests in detecting rotator cuff (RC) tear size and tendon reparability of large and massive tears.The purpose of this diagnostic study was to examine the validity of four strength-related clinical sign/tests in relation to RC tear size and reparability.Methods: This was a prospective blinded study of consecutive patients with a full thickness RC tear who underwent a repair.The magnetic resonance imaging (MRI) and arthroscopic surgery were used as the gold standards.Results: Eighty-five patients, 50 males (59%), age 65, SD=10 completed the study.There were 60 (71%) minor tears (small/ moderate) and 25 (29%) major tears (large/massive) with 70 (82%) patients achieving a full repair.The Jobe test had a sensitivity of 93% and 88% and a negative likelihood ratio (LR) of 0.16 and 0.27 for tendon reparability and tear size respectively.The dropping sign, hornblower sign and lift-off test had poor sensitivity (<60%) and high specificity (>98%) values with large positive LRs for tear size detection and tendon reparability.The validity indices in relation to MRI findings were similar to surgical findings.Conclusion: A negative Jobe test accurately ruled out the presence of a major tear, significant supraspinatus fatty infiltration and a need for partial repair.The dropping and hornblower signs and lift off test were highly specific and when positive, they confirmed the presence of a major tear, fatty infiltration in the corresponding muscle and difficulty achieving a full repair.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".