Prospective Relation between Catastrophizing and Residual Pain following Knee Arthroplasty: Two‐Year Follow‐Up
Bibliographic record
Abstract
BACKGROUND: Pain is the primary indication for both primary and revision total knee arthroplasty (TKA); however, most arthroplasty outcome measures do not take pain into account. OBJECTIVE: To document the prospective pain experience following TKA, with subjective pain-specific questionnaires to determine if comorbidities, preoperative pain or preoperative pain catastrophizing scores are predictive of long-term pain outcomes. METHODS: Fifty-five patients with a primary diagnosis of osteoarthritis of the knee, who were scheduled to undergo TKA, were asked to fill out the McGill Pain Questionnaire (MPQ) and the Pain Catastrophizing Scale (PCS) preoperatively and at three, 12 and 24 months follow-up. Comorbidities were extracted from the Queen Elizabeth II Health Sciences Centre health information system. RESULTS: The overall response rate (return of completed questionnaires) was 84%. There was a significant decrease in the MPQ scores (P<0.05) postoperatively. PCS scores did not change over time. Receiver operating characteristic curves revealed the number of comorbidities per patient predicted the presence of pain postoperatively, as documented by the numerical rating subscale of the MPQ at 24 months (P<0.05). Receiver operating characteristic curves for preoperative PCS and rumination subscale scores predicted the presence of pain, as measured by the Pain Rating Index subscale of the MPQ at 24 months (P<0.05). Preoperative PCS scores and comorbidities were significantly higher in the persistent pain group (P<0.05). CONCLUSIONS: The number of comorbidities predicted the presence of pain at 24 months follow-up and, for the first time, preoperative PCS scores were shown to predict chronic postoperative pain. This may enable the identification of knee arthroplasty patients at risk for persistent postoperative pain, thus allowing for efficient administration of preoperative interventions to improve arthroplasty outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".