Paper #42 Operative stabilization of traumatic posterior shoulder instability
Bibliographic record
Abstract
To evaluate the results of operative stabilization in patients with traumatic posterior instability of the shoulder. 23 consecutive patients underwent surgical stabilization for traumatic posterior instability of the shoulder. All patients were evaluated using physical exam, Rowe score, and outcome measures utilizing the WOSI (Western Ontario Shoulder Instability Index) and SANE (single assessment numeric evaluation) scores. 24 shoulders in 23 patients underwent posterior shoulder stabilization, 14 arthroscopically and 12 by open technique. All patients had a distinct traumatic etiology leading to the instability. All patients had posterior apprehension and increased posterior translation on preoperative physical exam. Preoperative imaging revealed posterior rim calcification or reverse Bankart lesions in 21 shoulders (87.5%). At arthroscopy, posterior labral lesions, reverse Bankart lesions, or humeral head defects were observed in all cases. Average follow-up was 30 months (range:10–52). The average SANE, WOSI, and Rowe Scores was 85, 976, and 82, respectively. Nineteen of twenty-four shoulders were rated good or excellent (80%) and returned to unrestricted sports and activity.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".