A Prospective Analysis of Opioid Prescription, Consumption, and Psychometric Correlations in Outpatient Plastic Surgery Procedures
Bibliographic record
Abstract
Background: Understanding opioid prescription, consumption, and the factors related to these is important to prescribe opioids responsibly. Our primary purpose is to determine the factors predicting opioid prescription, and the secondary purpose is to examine the factors predicting opioid tablet consumption. Methods: A prospective cohort was evaluated using 2 surveys. The primary outcome was type of prescription given (opioid vs non-opioid). The secondary outcome was the number of opioid tablets consumed at the second survey. Demographics, the pain catastrophizing scale, and patient health questionnaire-4 (PHQ-4) for depression and anxiety were collected. Statistics included Chi-Square, student's t-test, univariable, and multivariate regression analyses. Results: Four hundred and forty patients completed the first survey, of which 193 completed the second. Two-hundred and fourteen (49%) patients received an opioid prescription. Opioids were given most often after: surgery in the main operating room (OR 23.6 [10.0-55.2]), breast or abdomen (OR 11.1 [1.2-101.1]), upper limb (OR 4.0 [1.7-9.3]), and less often after dermatologic surgery (OR 0.2 [0.1-0.5]). Among patients who received opioids, a mean of 10 opioid tablets were consumed at the post-operative survey. More tablets were consumed when: age was less than 60 ( P < .05), with pre-operative opioid use ( P = .03), and with a high score on the PHQ-4 ( P = .002). Conclusions: The patterns of opioid prescription and consumption after outpatient Plastic Surgery are elucidated. Plastic surgeons over-estimate patients’ opioid requirements. Potentially less opioids could be prescribed in the minor procedure room without an increase in pain crises. Public health campaigns should focus on the proper disposal of unused opioid tablets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".