Neuropathic Pain after Shoulder Arthroplasty: Prevalence, Impact on Physical and Mental Function, and Demographic Determinants
Bibliographic record
Abstract
Purpose: The objectives of this survey study were to provide an estimate of the prevalence of neuropathic pain (NP) and to explore the cross-sectional and longitudinal group differences postoperatively. Method: A cohort of consecutive patients who had undergone total shoulder arthroplasty (TSA), reverse shoulder arthroplasty (RSA), or humeral head replacement (HHR) were surveyed within an average of 3.8 years after surgery. Questionnaires completed at the time of the survey were the Self-Administered Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) pain scale, the visual analogue scale (VAS) for pain, the Western Ontario Osteoarthritis of the Shoulder (WOOS) index, the Patient Health Questionnaire–9 (PHQ–9), and a satisfaction questionnaire. Results: Of the 141 candidates who were invited to participate in the study, 115 patients participated (85 TSA, 21 HHR, and 9 RSA), for an 82% response rate. Five patients (4%) met the criteria for NP, of whom one had a loosening of the prosthesis and required further surgery. Having NP was associated with greater pain (VAS; p=0.001), greater depression (PHQ–9; p=0.001), more disability (WOOS; p=0.030), and less satisfaction with the surgery (p=0.014). There was no relationship between the presence of NP and patients' age, sex, preoperative pain, range of motion results, or WOOS scores (p>0.05). Conclusions: Persistent pain of neuropathic origin is not common after shoulder arthroplasty, but it is a significant contributor to poor mental and physical well-being and thus warrants further research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".