Hypervigilance as Predictor of Postoperative Acute Pain: Its Predictive Potency Compared With Experimental Pain Sensitivity, Cortisol Reactivity, and Affective State
Bibliographic record
Abstract
OBJECTIVES: Pain hypervigilance--a strong attentional bias toward pain--is thought to accompany chronic pain and modulate pain management. Its usefulness as predisposing factor for the development and maintenance of pain has been discussed. The aim of our study was to demonstrate the predictive power of hypervigilance for the development of acute postoperative pain. METHODS: Fifty-four young male patients were assessed 1 day before surgery (correction of chest malformation) on a range of psychologic predictors. These predictors included the assessment of hypervigilance (questionnaires as the Pain Catastrophizing Scale, Pain Anxiety Symptom Scale, the Pain Vigilance and Awareness Questionnaire, and the dot-probe task) and affective state, experimental pain sensitivity, and cortisol reactivity. Acute postoperative pain was assessed by ratings of pain intensity 1 week postsurgery and through the amount of analgesics [patient-controlled epidural analgesia (PCEA)] requested during the first days after surgery. RESULTS: Pain intensity was significantly explained (17% explained variance) by hypervigilance, whereas PCEA performance was not (10%). Adding all other predictors led to a significant increase of explained variance (35%) for pain ratings and a nonsignificant increase (19%) for PCEA. A more parsimonious solution with only heat pain threshold added led to a significant increase in explained variance (30%) for pain intensity. Hypervigilance was only moderately correlated with the other predictors. DISCUSSION: Hypervigilance proved to be a powerful predictor of subjective acute postoperative pain, but was less useful with regard to the amount of requested analgesics. The overlap with other psychologic predictors (affective state, experimental pain sensitivity, and cortisol reactivity) is sufficiently small to consider hypervigilance a promising supplement in psychologic predictor research.
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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".