Characteristics of Fibromyalgia Independently Predict Poorer Long‐Term Analgesic Outcomes Following Total Knee and Hip Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: While psychosocial factors have been associated with poorer outcomes after knee and hip arthroplasty, we hypothesized that augmented pain perception, as occurs in conditions such as fibromyalgia, may account for decreased responsiveness to primary knee and hip arthroplasty. METHODS: A prospective, observational cohort study was conducted. Preoperative phenotyping was conducted using validated questionnaires to assess pain, function, depression, anxiety, and catastrophizing. Participants also completed the 2011 fibromyalgia survey questionnaire, which addresses the widespread body pain and comorbid symptoms associated with characteristics of fibromyalgia. RESULTS: Of the 665 participants, 464 were retained 6 months after surgery. Since individuals who met criteria for being classified as having fibromyalgia were expected to respond less favorably, all primary analyses excluded these individuals (6% of the cohort). In the multivariate linear regression model predicting change in knee/hip pain (primary outcome), a higher fibromyalgia survey score was independently predictive of less improvement in pain (estimate -0.25, SE 0.044; P < 0.00001). Lower baseline joint pain scores and knee (versus hip) arthroplasty were also predictive of less improvement (R(2) = 0.58). The same covariates were predictive in the multivariate logistic regression model for change in knee/hip pain, with a 17.8% increase in the odds of failure to meet the threshold of 50% improvement for every 1-point increase in fibromyalgia survey score (P = 0.00032). The fibromyalgia survey score was also independently predictive of change in overall pain and patient global impression of change. CONCLUSION: Our findings indicate that the fibromyalgia survey score is a robust predictor of poorer arthroplasty outcomes, even among individuals whose score falls well below the threshold for the categorical diagnosis of fibromyalgia.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".