Catastrophizing: A Risk Factor For Postsurgical Pain
Bibliographic record
Abstract
OBJECTIVE: This research was designed to test the hypothesis that presurgery "catastrophizing" would predict postsurgical pain and postsurgical analgesic consumption. METHODS: A sample of 48 individuals who underwent anterior cruciate ligament repair participated in the study. All participants completed the Pain Catastrophizing Scale (described by Sullivan et al in 1995) prior to surgery. Measures of pain (pain scores on a scale of 0-10) were obtained in the postanesthetic care unit, as well as 1, 2, and 7 days after surgery. Opioid and nonopioid analgesic consumption was tabulated while patients were in the hospital and after discharge. RESULTS: Results showed that the Pain Catastrophizing Scale was a significant predictor of acute postsurgical pain in the postanesthetic care unit (r = 0.48, P = 0.004 for maximum pain in the postanesthetic care unit). Maximum pain ratings in patients with high Pain Catastrophizing Scale scores (> median of 13) were 33% to 74% higher numerically than in patients with low Pain Catastrophizing Scale scores (< or = median), and the duration of moderate-severe pain (>3/10) was more prolonged (45 minutes versus 28 minutes in patients with high and low Pain Catastrophizing Scale scores, respectively; P < 0.05). The Pain Catastrophizing Scale was also predictive of pain with activity at 24 hours (r = 0.65 for pain on walking, P < or = 0.0001). The Pain Catastrophizing Scale did not predict postoperative analgesic use. CONCLUSION: The pattern of findings suggests that high catastrophizing scores may be a risk factor for heightened pain following surgery. Clinical and theoretical implications of the findings are addressed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".