Perspectives of Facial Plastic Surgeons on Opioid Dependence in Rhinoplasty Patients
Bibliographic record
Abstract
Understanding the perspectives and opinions of facial plastic surgeons on opioid dependence is critical in a national epidemic of opioid overuse. Findings may encourage surgeon education so that facial plastic surgeons may be able to judiciously prescribe opioids, improving patient outcomes and reducing healthcare opioid-related spending. The objective of this study is to understand facial plastic surgeons' perspectives on opioid dependence in rhinoplasty patients. A key secondary objective was to quantify facial plastic surgeons' opioid prescribing patterns. This was a prospective survey study. A nine-question survey was sent to all members of the American Academy of Facial Plastic and Reconstructive Surgery in July of 2018, and analysis of the data was completed in August of 2018. The primary study outcome measurement was surgeon perspectives on opioid dependence. This was measured by an online survey. A total of 164 facial plastic surgeons responded to the survey (response rate: 6.6%). The majority were experienced surgeons in practice for more than 10 years (61.96%) who perform less than five rhinoplasties per week (84.15%). Of the facial plastic surgeons, 89.51% prescribe some variation of opioids following rhinoplasty. Most surgeons believe that opioid dependence is not a problem in rhinoplasty patients (86.96%), but that it is a problem among surgical patients in general (61.11%). The majority (52.45%) of surgeons prescribe between 11 and 25 tablets of opioids following rhinoplasty, with 25.17% of surgeons prescribing > 25 tablets of opioids. Facial plastic surgeons do not believe opioid dependence to be a problem among rhinoplasty patients. Resultantly, many facial plastic surgeons can prescribe more than 25 tables of opioids following rhinoplasty. The findings suggest that facial plastic surgeons may require further education and complete more research regarding opioid dependence among the rhinoplasty population. Additionally, the findings are important for health policy in that they encourage the creation of rhinoplasty specific opioid prescription guidelines.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".