Psychosocial Factors, Sleep, and Central Pain Processing for Making a Prognosis About Recovery of Pain, Function, and Quality of Life After Rotator Cuff Repair: An Exploratory Longitudinal Study
Bibliographic record
Abstract
OBJECTIVE: To explore modifiable psychosocial factors, sleep-related variables, indices of central pain processing and patients’ characteristics as potential prognostic factors for pain, shoulder function, and quality of life (QoL) 1 year after rotator cuff repair. DESIGN: This observational longitudinal study included 142 patients who were undergoing rotator cuff repair. All measures took place pre–rotator cuff repair (T0), and 12 weeks (T1) and 12 months (T2) after rotator cuff repair. METHODS: Mixed-effects linear regression modeled relationships between the Western Ontario Rotator Cuff Index (WORC, model A), the Subjective Shoulder Value (SSV, model B), and EuroQol’s EQ-5D-5L for QoL (model C), and potential prognostic factors over time. Factors included psychosocial variables, sleep-related indices, and proxies of central pain processing. Patients’ age, sex, and body mass index complemented the analyses. RESULTS: At follow-up (T2), data from 124 participants were available for analysis. Five prognostic factors were identified for the 1-year outcome. Better expectations for symptom reduction ( P<.0001, −1.4 mm) and an increase in Douleur Neuropathique 4 score ( P = .0481, −0.9 mm) affected the evolution of WORC over time (model A). An increase in injury perception subscale consequence ( P = .0035, 0.04%) influenced the SSV trajectory (model B). In addition, when sleep quality ( P = .0011, −0.13%) and sleep efficiency ( P = .0002, 0.005%) improved, the EQ-5D-5L slope was affected (model C). CONCLUSION: Addressing cognitions, pain mechanisms and sleep behavior prior to rotator cuff repair can identify people who are at risk of a poor outcome after surgery. J Orthop Sports Phys Ther 2024;54(8):530-540. Epub 4 July 2024. doi:10.2519/jospt.2024.12398
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".