Rotator Manşet Yırtığı Onarımı İçin Çift Sıra Tamir Tekniği Yerine Tek Sıra Tamir Tekniği Kullanılarak Başarılı Sonuçlar Elde Edilebilir Mi?
Bibliographic record
Abstract
Aim: To compare the outcomes of patients grouped according to the intraoperative size of the anteroposterior tear treated using double- or single-row repair techniques. Material and methods: We examined the outcomes of 112 patients who met our inclusion/exclusion criteria by using the preoperative and postoperative Constant scores. We divided the patients treated using single- or double-row techniques into 4 groups based on the intraoperative size of the anteroposterior tear, including both the supraspinatus and infraspinatus tears. Further, we divided the patients in these 4 groups into two additional subgroups treated using single- and double-row techniques. Results: The single-row group included 64 patients and the double-row group included 48 patients. The mean follow-up time for the single- and double-row groups was 35.6 and 33.5 months, respectively. We observed a significant improvement in the outcomes of patients in the single- and double-row groups; the preoperative and postoperative Constant scores of patients in the single-row groups were 36 and 81.2, respectively (p = 0.00001). The preoperative and postoperative Constant scores of patients in the double-row groups were 31.6 and 74.3, respectively (p = 0.00001). Patients with an intraoperative tear size of 1-3 cm treated using the single-row technique showed better outcomes than those treated using the double-row technique (postoperative Constant scores 81.2 and 71.86, respectively, p = 0.00585). Conclusion: Thus, the single-row repair technique was used successfully in patients with supraspinatus and infraspinatus tears ranging from 1-3 cm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".