Improving pre-operative MRI diagnosis of peroneal tendon tears with a new objective sign and assessing the value of peroneus brevis fatty atrophy
Bibliographic record
Abstract
Objectives: We aimed to compare the diagnostic performance of an objective set of magnetic resonance imaging (MRI) criteria named the cleft sign with traditional reads for pre-operative diagnosis of peroneal tendon tears. We also investigated the relationship between peroneus brevis tendon tears and muscle quality. Material and Methods: Two blinded readers retrospectively and independently evaluated pre-operative ankle MRI studies of 38 patients who had undergone peroneal tendon surgery for peroneal tendon tears, peroneus brevis muscle quality, and the cleft sign. MRI examinations from 38 control subjects were also reviewed for peroneus brevis muscle quality. The diagnostic performances of MRI for peroneal tendon tears with and without application of the cleft sign were analyzed. The correlation between peroneus brevis fatty atrophy and tendon tears was also examined. Results: In patients without prior peroneal surgery, the sensitivity and specificity of MRI for peroneus brevis tendon tears were 60%/89% and 80%/78% for readers 1 and 2, respectively. Application of the cleft sign significantly increased sensitivity in reader 1 to 95%, with a non-significant increase in accuracy in both readers. The interobserver reliability for the cleft sign was moderate for peroneus brevis (κ = 0.57). No significant difference in peroneus brevis muscle quality was present between subjects with and without peroneus brevis tendon tears and between surgical and control patients. Conclusion: In patients without prior peroneal tendon surgery, the cleft sign can significantly improve reader diagnostic sensitivity for peroneus brevis tears. The muscle quality of the peroneus brevis has limited value in MRI diagnosis of peroneal tendon tears.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".