Selective processing of threat‐related cues in day surgery patients and prediction of post‐operative pain
Bibliographic record
Abstract
OBJECTIVE: To investigate the use of a measure of selective processing bias associated with anxiety as a predictor of post-operative pain independently of self-report measures of anxiety. METHODS: Forty-seven women admitted for minor gynaecological surgical procedures completed a selective processing task (modified Stroop) and the State-Trait Anxiety Inventory immediately prior to surgery. Following surgery they completed the McGill Short-Form Pain Questionnaire. Intraoperative analgesia consumption was also recorded. RESULTS: Participants demonstrated significantly slower colour-naming times for physical threat cues than control cues. This was not due to an emotionality effect, as colour-naming times for neutral and positive cues were not significantly different. This bias was congruent with the participants' current concerns, as colour-naming times were significantly slower for physical threat words than for social threat words. This index of selective processing bias significantly predicted post-operative pain independently of self-reported state and trait anxiety. CONCLUSIONS: The advantages of measures of psychological constructs that are not reliant on self-reporting are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".