Outcomes of the Latarjet Procedure in Recurrent Anterior Shoulder Instability due to Tramadol-Induced Seizure
Bibliographic record
Abstract
Background: This study was aimed to evaluate the final results of surgical treatment (Latarjet procedure) in the recurrent anterior shoulder instability following episodes of tramadol-induced seizure. Methods: From January 2005 to March 2013, 47 patients with recurrent anterior shoulder dislocation after suffering a seizure episode following tramadol use underwent surgical procedure. There were 53 shoulders in 47 male patients (six had bilateral recurrent dislocations). The mean age of the patients at the time of operation was 24.7 years (ranging from 20 to 44 years). The average number of episodes of anterior shoulder dislocation before surgery was 16. Results: External rotation with the elbow at the side improved from 45.8 ± 9.3° (30°-60°) pre-operatively to 61.5 ± 7.8° (45°-90°) postoperatively (P < 0.001). Forward elevation also increased significantly post-operatively (P = 0.002). Mean pre-operative Rowe score was 28.41 ± 4.30 (30-85) which increased to 73.57 ± 8.40 post-operatively. The Western Ontario Shoulder Instability Index (WOSI) score decreased from 1352 ± 74 to 618 ± 46 (P < 0.0001). Conclusion: Correcting glenoid bone loss by Latarjet procedure combined, if necessary, with humeral head defect reconstruction could be a proper treatment method in patients experiencing recurrent anterior shoulder dislocation after idiosyncratic seizure reaction of tramadol.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".