Do nonsteroidal anti‐inflammatory drugs affect the outcome of arthroscopic <scp>B</scp>ankart repair?
Bibliographic record
Abstract
To achieve pain control after arthroscopic shoulder surgery, nonsteroidal anti-inflammatory drugs (NSAIDs) are a complement to other analgesics. However, experimental studies have raised concerns that these drugs may have a detrimental effect on soft tissue-to-bone healing and, thus, have a negative effect on the outcome. We wanted to investigate if there are any differences in the clinical outcome after the arthroscopic Bankart procedure for patients who received NSAIDs prescription compared with those who did not. 477 patients with a primary arthroscopic Bankart procedure were identified in the Norwegian shoulder instability register and included in the study. 32.5% received prescription of NSAIDs post-operatively. 370 (78%) of the patients answered a follow-up questionnaire containing the Western Ontario Shoulder Instability index (WOSI). Mean follow-up was 21 months. WOSI at follow-up were 75% in the NSAID group and 74% in the control group. 12% of the patients in the NSAID group and 14% in the control group reported recurrence of instability. The reoperation rate was 5% in both groups. There were no statistically significant differences between the groups. Prescription of short-term post-operative NSAID treatment in the post-operative period did not influence on the functional outcome after arthroscopic Bankart procedures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".