The Effect of Paratendinous Aprotinin Injection in Patients with Rotator Cuff Tendinitis
Bibliographic record
Abstract
Objective: To determine the therapeutic effect of paratendinous injection of aprotinin, a polyvalent inhibitor of inflammatory proteolytic enzyme, in patients with shoulder tendinitis. Method: Thirty patients with shoulder tendinitis diagnosed with ultrasonography were included. Patients were assigned to one of two groups at random to receive paratendinous injection. One group received a paratendinous aprotinin 1.5 ml and 1% lidocaine 2 ml injection of shoulder 2∼5 times at 1 week apart. The other group received a paratendinous injection one time with mixture of triamcinolone 40 mg and 1% lidocaine 2.5 ml. The effect of treatment was assessed with the visual analogue scale (VAS), and the patients' life activities were assessed with the Western Ontario rotator cuff (WORC) index. Results: The VAS of the two groups showed improvement at 1 week (aprotinin group: 2.9±0.7, triamcinolone group: 3.7±1.2) and 4 weeks (aprotinin group: 2.1±1.0, triamcinolone group: 2.4±1.0) after injection compared with pre- injection status (aprotinin group: 8.6±1.3, triamcinolone group: 8.2±1.3)(p＜0.01) and the WORC index of the two groups showed improvement at 1 week (aprotinin group: 36.5±7.8, triamcinolone group: 53.2±12.3) and 4 weeks (aprotinin group: 33.4±6.2, triamcinolone group: 31.4±8.8) after injection compared with pre-injection status (aprotinin group: 116.2±29.1, triamcinolone group: 123.5±37.0)(p＜ 0.01). There was no significant difference in the improvement of the VAS scores and WORC index between the two groups. Conclusion: The short term effect of paratendinous aprotinin injection in patients with shoulder tendinitis was as good as triamcinolone injection, although more frequent injection was necessary. (J Korean Acad Rehab Med 2008; 32: 56-61)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".