Artroskopik rotator manşet onarımı yapılan hastalarda klinik sonuçları ayırt etmek için hastalığa özgü yaşam kalitesine yönelik optimum kesme puanları
Bibliographic record
Abstract
Objective: Identifying a cut-off score would be useful in detecting improvements in disease-specific quality of life (DS-QoL) in patients with arthroscopic rotator cuff repair (ARCR). The aim of this study was to identify clear cut-off values for the Western Ontario Rotator Cuff Index (WORC) score. In addition, the ability of these cut-off scores to predict DS-QoL level was investigated. Method: A total of 38 ARCR patients were included in this cross-sectional study. Patients were assessed using the Constant-Murley and WORC scores following 12 weeks of physiotherapy. Pearson correlation coefficients were used to analyse the relationship between these scores. The WORC cut-off scores representing excellent and good DS-QoL were calculated on the basis of the Constant score. The ability of the these cut-offs to predict the level of DS-QoL was examined using logistic regression analysis. Results: The WORC cut-off scores of 87.5 and 79.5 were found to be excellent and good level of DS-QoL. Participants with WORC scores above these cut-offs have a 1.25 and 1.74 times higher level of DS-QoL, respectively. Conclusion: The success of physiotherapy and ARCR could be assessed using the identified cut-off scores. It seems necessary to use more specific interventions for patients not meeting the WORC cut-off scores in order to improve DS-QoL.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".