Bony Bankart is a positive predictive factor after primary shoulder dislocation
Bibliographic record
Abstract
It would be a great advantage if it were possible to categorise the patients with first time dislocations to an initial treatment with the most beneficial outcome. MRI could be a useful method for finding lesions after shoulder dislocation. Fifty-eight patients with traumatic anterior shoulder dislocation were treated by closed reduction and were examined by MRI after a maximum of 2 weeks. The hemarthrosis or effusion present in the joint after the primary dislocation could be used as a contrast for arthrography to identify the lesions present on MRI. At follow-up more than 8 years later, the MRI findings were compared to the shoulder function, shoulder stability, Rowe score and Western Ontario Shoulder Instability Index (WOSI). Besides the age of the patient being above 30, the MRI findings analysed showed that an isolated fracture of the major tubercle, as well as a bony Bankart lesion are prognostic factors for a good functional result and a stable shoulder after a primary dislocation. The glenoid rim fracture was only detected on plain radiographs in 6 out of 10 findings on MRI. MRI findings of a gleniod rim fracture, equal to a bony Bankart lesion, were found to be a prognostic factor for stability and a good functional outcome.
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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 | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".