Presurgical distress about bodily sensations predicts chronic postsurgical pain intensity and disability 6 months after cardiothoracic surgery
Bibliographic record
Abstract
ABSTRACT: Chronic postsurgical pain (CPSP) and disability after cardiothoracic surgery are highly prevalent and difficult to treat. Researchers have explored a variety of presurgical risk factors for CPSP and disability after cardiothoracic surgery, including one study that examined distress from bodily sensations. The current prospective, longitudinal study sought to extend previous research by investigating presurgical distress about bodily sensations as a risk factor for CPSP and disability after cardiothoracic surgery while controlling for several other potential psychosocial predictors. Participants included 543 adults undergoing nonemergency cardiac or thoracic surgery who were followed over 6 months postsurgically. Before surgery, participants completed demographic, clinical, and psychological questionnaires. Six months after surgery, participants reported the intensity of CPSP on a 0 to 10 numeric rating scale and pain disability, measured by the Pain Disability Index. Multinomial logistic regression analyses were conducted to evaluate the degree to which presurgical measures predicted pain outcomes 6 months after surgery. The results showed that CPSP intensity was significantly predicted by age and presurgical scores on the Symptom Checklist-90-Revised Somatization subscale (Nagelkerke R2 = 0.27, P < 0.001), whereas chronic pain disability was only predicted by presurgical Symptom Checklist-90-Revised Somatization scores (Nagelkerke R2 = 0.29, P < 0.001). These findings demonstrate that presurgical distress over bodily sensations predicts greater chronic pain intensity and disability 6 months after cardiothoracic surgery and suggest that presurgical treatment to diminish such distress may prevent or minimize CPSP intensity and disability.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".