Accuracy of magnetic resonance imaging to diagnose superior labrum anterior–posterior tears
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the accuracy of magnetic resonance imaging (MRI) and magnetic resonance arthrography (MRA) in diagnosing superior labral anterior-posterior (SLAP) lesions. We hypothesized that the accuracy of MRI and MRA was lower than previously reported. METHODS: Between 2006 and 2008, 444 patients who had both shoulder arthroscopy and an MRI (non-contrast or MR arthrography) for shoulder pain at our institution prior to surgery were identified and included in the study. The radiologic diagnosis and surgical evaluation were compared to determine the accuracy of diagnosing a SLAP lesion by MRI. Using arthroscopy as the standard, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated for all MRIs, as well as separately for the non-intra-articular contrast MRI group and the MR arthrography group. RESULTS: Of the 444 patients having an MRI and arthroscopy for shoulder pain, 121 had a SLAP diagnosis by MRI and 44 had a SLAP diagnosis by arthroscopy. Overall, MRI had an accuracy of 76 %, a PPV of 24 %, and a NPV of 95 %. Sensitivity was 66 %, and specificity was 77 %. MR arthrography had an accuracy of 69 %, sensitivity of 80 %, and a PPV of 29 %. Non-contrast MRI had an accuracy of 85 %, sensitivity of 36 %, and a PPV of 13 %. CONCLUSIONS: In our retrospective study of 444 patients, sensitivity, specificity, and accuracy were all lower than previously reported in the literature for diagnosing SLAP lesions. Our data indicated that while MRI could exclude a SLAP lesion (NPV = 95 %), MRI alone was not an accurate clinical tool. MR arthrography had a large number of false-positive readings in this study. We concluded that even with intra-articular contrast, MRI had limitations in the ability to diagnose surgically proven SLAP lesions.
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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 | 0.005 | 0.050 |
| 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) | 0.001 | 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".