Predictors of Perioperative Opioid Use in Hysterectomy Patients
Bibliographic record
Abstract
Background and Objectives: Little is known about predictors of opioid use in the acute postoperative phase after hysterectomy. Inadequate pain support during this time can result in increased postoperative complications, and persistent postoperative pain. Objective is to determine predictors of increased opioid use in the acute perioperative phase (intraoperatively and 1 hour and 24 hours postoperatively). Methods: A prospective cohort study involving 200 participants undergoing nonurgent hysterectomy via laparoscopic, vaginal, abdominal, or robotic approaches at an academic tertiary hospital in Toronto, Canada. Data collected included demographics, preoperative validated pain questionnaire scores, pain scores at 1 and 24 hours postoperatively, and analgesic medications used. Nonparametric statistical methods and multivariate analyses were used to examine the association between clinical predictors and opioid use. Opioid use was converted into morphine equivalent dose (MED). Results: Pain sensitivity questionnaire (PSQ) score and body mass index were strongly associated with increased intraoperative MED. Twenty-four-hour postoperative opioid use was negatively correlated to age. Multivariate analysis identified PSQ total score and open hysterectomy as predictors of higher intraoperative MED. The number of preoperative pain medications, open hysterectomy, and PSQ total score were significant predictors of total MED requirements. One additional pain medication and one additional total PSQ point were associated with an increase in total MED of 10.76 and 5.17 mg, respectively. Conclusions: This study is the first step in identifying clinical predictors of increased opioid requirements in the first 24 hours postoperatively. These predictors can inform patient-tailored management plans to ensure adequate pain support and appropriate opioid use.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".