A Cross-Sectional Survey on Prevalence and Risk Factors for Persistent Postsurgical Pain 1 Year After Total Hip and Knee Replacement
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is a paucity of large multi-institutional surveys to determine the prevalence of and risk factors for persistent pain after total hip (THR) and knee (TKR) replacements. We surveyed a variety of practices and patients and also correlated persistent pain with health-related quality-of-life outcomes. METHODS: From October 10, 2007, to March 15, 2010, patients who had undergone primary THR or TKR with a minimum follow-up of 1 year were identified. A previously published questionnaire to identify persistent postsurgical pain that included a 36-item Short Form Health Survey was mailed to this group. Independent risk factors for persistent pain were identified with logistic regression. RESULTS: Responses from 1030 patients who underwent surgery at some point in time between June 13, 2006, and June 24, 2009, were analyzed (32% response rate). Forty-six percent of patients reported persistent pain (38% after THR and 53% after TKR) with a median average pain score of 3 of 10 and worst pain score of 5. Independent risk factors for persistent pain were female sex (odds ratio [OR], 1.23), younger age (OR, 0.97), prior surgery on hip or knee (OR, 1.39), knee versus hip replacement (OR, 1.65), lower-quality postsurgical pain control (OR, 0.9), and presence of pain in other areas of the body (OR, 2.09). All scores in the 36-item Short Form Health Survey were worse (8%-28% decrease) in patients with persistent postsurgical pain (P < 0.001). CONCLUSIONS: Persistent postsurgical pain is common after THR and TKR and is associated with reduced health-related quality of life, although our survey may be biased by the low response rate and retrospective recall bias. Nonmodifiable risk factors may lead to risk stratification. Severity of acute postoperative pain may be a modifiable risk factor.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".