Preoperative management of patients with chronic moderate to severe shoulder pain to improve postoperative outcomes: A systematic review
Bibliographic record
Abstract
OBJECTIVES: To assess if implementing interventions to effectively manage preoperative chronic moderate to severe shoulder pain in patients undergoing rotator cuff repair (RCR) can improve shoulder surgery outcomes. METHODS: A systematic review was conducted following the PRISMA and SIGN guidelines. Randomized clinical trials (RCTs), metanalysis, systematic revisions and cohort studies in Spanish/English, published within the last 10 years, evaluating interventions to control preoperative chronic moderate to severe shoulder pain in patients undergoing RCR and their impact in postoperative shoulder outcomes were included. Selected records were graded following the 2011 Oxford Centre for Evidence-Based Medicine levels of evidence (OCEBML). RCTs were graded using the PEDro scale. RESULTS: Twenty-nine records were included in the analysis. Evidence suggests that preoperative chronic moderate to severe shoulder pain is the strongest risk factor for postoperative shoulder pain (OCEBML III). Patient-related factors and shoulder pain characteristics can also influence surgery outcomes (OCEBML II/III). Predictors of better shoulder function at 2 years after surgery include higher preoperative scores on the Western Ontario Rotator Cuff index and the Constant-Murley score in the contralateral shoulder (OCEBML III). Preoperative analgesia to control shoulder pain can improve postoperative pain (OCEBML I). Preoperative patient teaching and intensive postoperative follow-up also improve pain intensity and function (OCEBML II). DISCUSSION: Preoperative chronic shoulder pain together with patient-related factors are significant predictors of postoperative shoulder outcomes, emphasizing the need for proactive pain assessment and tailored therapeutic programs.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".