Opioid Prescriptions Following Otologic Surgery: A Population‐Based Study
Bibliographic record
Abstract
OBJECTIVE: To examine postoperative opioid-prescribing patterns following otologic surgery. STUDY DESIGN: Retrospective population-based descriptive study. SETTING: All hospitals in the Canadian province of Ontario. METHODS: Of all patients with advanced ear surgery between July 1, 2012, and March 31, 2019, 7 cohorts were constructed: tympanoplasty with or without ossiculoplasty (n = 7812), atticotomy/limited mastoidectomy (n = 1371), mastoidectomy (n = 3717), semicircular canal occlusion (SCO; n = 179), stapedectomy (n = 2735), bone-implanted hearing aid insertion (n = 280), and cochlear implant (n = 2169). Prescriptions filled for narcotics postoperatively were calculated per morphine milligram equivalent (MME) opioid dose. Multivariable regression was used to determine predictors of higher opioid doses. RESULTS: The mean ± SD MMEs prescribed were as follows: tympanoplasty with or without ossiculoplasty, 246.77 ± 1380.78; atticotomy/limited mastoidectomy, 283.32 ± 956.10; mastoidectomy, 280.56 ± 1018.50; SCO, 328.61 ± 1090.86; stapedectomy, 164.64 ± 657.18; bone-implanted hearing aid insertion, 326.11 ± 1054.66; and cochlear implant, 200.87 ± 639.93. SCO (odds ratio [OR], 1.69 [95% CI, 1.16-2.48]) and mastoidectomy (OR, 1.50 [95% CI, 1.36-1.66]) were associated with higher opioid doses than tympanoplasty-ossiculoplasty. Asthma (OR, 1.24 [95% CI, 1.12-1.38]), chronic obstructive pulmonary disease (OR, 1.29 [95% CI, 1.12-1.47]), myocardial infarction (OR, 1.33 [95% CI, 1.05-1.68]), diabetes (OR, 1.22 [95% CI, 1.08-1.39]), and substance-related and addictive disorders (OR, 2.59 [95% CI, 1.67-4.00]) were associated with higher opioid doses prescribed. Overall MME prescribed by year demonstrates a sharp drop from 2017-2018 to 2018-2019. CONCLUSION: This large comprehensive population study provides insight into the prescribing patterns following otologic surgery. The large amounts prescribed and substantial variation require further study to determine barriers that limit good opioid-prescribing stewardship in the postoperative period.
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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 | 0.000 |
| 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) | 0.001 | 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".