Prediction of post‐operative pain after a laparoscopic tubal ligation procedure
Bibliographic record
Abstract
BACKGROUND: Pre-operative identification of reliable predictors of post-operative pain may lead to improved pain management strategies. We investigated the correlation between pre-operative pain, psychometric variables, response to heat stimuli and post-operative pain following a laparoscopic tubal ligation procedure. METHODS: Assessments of anxiety, mood, psychological vulnerability and pre-operative pain were made before surgery using the State-Trait Anxiety Inventory (STAI), the Hospital Anxiety Depression Scale (HADS), a psychological vulnerability test and the Short-Form McGill Pain Questionnaire (SF-MPQ), respectively. Pre-operative assessments of thermal thresholds and pain response to randomized series of heat stimuli (1 s, 44-48 degrees C) were made with quantitative sensory testing technique. Post-operative pain intensity was evaluated daily by a visual analogue scale during rest and during standardized dynamic conditions for 10 days following surgery. Univariate and multivariate regression analyses were used to construct prediction models. RESULTS: Fifty-nine patients completed the study. Post-operative pain was significantly correlated with pre-operative pain (SF-MPQ), heat pain perception, psychological vulnerability, STAI and HADS. In the multiple regression model pre-operative pain and heat pain perception were significant predictive factors (R=0.537-0.609). CONCLUSION: The study indicates that pre-surgical pain and heat pain sensitivity are important pre-operative indicators of post-operative pain intensity, while psychological factors like vulnerability and anxiety seem to contribute to a lesser degree after laparoscopic tubal ligation. The prediction model accounted for 29-43% of the total variance in post-operative movement-related pain.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".