Potential Moderators For Outcome After Rotator Cuff Repair : a Pilot Study
Bibliographic record
Abstract
Background and aimThere is a growing body of evidence, that central pain processing (CPP) and psychosocial factors may maintain or drive shoulder pain. In patients undergoing rotator cuff repair (RCR,) findings report that patientsu2019 expectations may predict outcome after surgery 1. Yet, there is a lack of evidence on modifiable factors that potentially moderate the outcome after RCR. The aim refers to identify such moderators for outcome after RCR. MethodsThe longitudinal study will investigate 141 datasets of adult patients undergoing RCR at Kantonsspital Winterthur, Switzerland. Mean change of three measurement points, 1. pre-operative, 2. 12 weeks post-operative, 3. 12 months post-operative of primary (Western Ontario Rotator Cuff Index (WORC)) and secondary outcome measures (Constant u2013 Score, maximum pain and quality of life) will be analysed by mixed-effects regression model for repeated measures. Stepwise inclusion of the 6 potential moderators will be conducted using linear and logistic regression models. Potential moderators are obtained by quantitative sensory testing and central sensitisation inventory (CPP), pain catastrophizing scale, illness perception questionnaire, perceived stress scale and questions about expectations and sleep.ResultsAssessments started in September 2018. Preliminary results from the pilot study will be available in summer 2019 and may provide insight in short-term prognosis for outcome 12 weeks postoperative. ConclusionResults may disclose moderators for outcome after RCR and foster a more patient-centred treatment approach with the aim to tailor the course of care towards a beneficial outcome. Funding acknowledgementAcknowledgments refer to the staff of Kantonsspital Winterthur. Ethical approval ID: 2018-02089.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".