Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: While persistent opioid use after surgery has been the subject of a large number of studies, it is unknown how much variability in the definition of persistent use impacts the reported incidence across studies. The objective was to evaluate the incidence of persistent use estimated with different definitions using a single cohort of postoperative patients, as well as the ability of each definition to identify patients with opioid-related adverse events. METHODS: The literature was reviewed to identify observational studies that evaluated persistent opioid use among opioid-naive patients requiring surgery, and any definitions of persistent opioid use were extracted. Next, the authors performed a population-based cohort study of opioid-naive adults undergoing 1 of 18 surgical procedures from 2013 to 2017 in Ontario, Canada. The primary outcome was the incidence of persistent opioid use, defined by each extracted definition of persistent opioid use. The authors also assessed the sensitivity and specificity of each definition to identify patients with an opioid-related adverse event in the year after surgery. RESULTS: Twenty-nine different definitions of persistent opioid use were identified from 39 studies. Applying the different definitions to a cohort of 162,830 opioid-naive surgical patients, the incidence of persistent opioid use in the year after surgery ranged from 0.01% (n = 10) to 14.7% (n = 23,442), with a median of 0.7% (n = 1,061). Opioid-related overdose or diagnosis associated with opioid use disorder in the year of follow-up occurred in 164 patients (1 per 1,000 operations). The sensitivity of each definition to identify patients with the composite measure of opioid use disorder or opioid-related toxicity ranged from 0.01 to 0.36, while specificity ranged from 0.86 to 1.00. CONCLUSIONS: The incidence of persistent opioid use reported after surgery varies more than 100-fold depending on the definition used. Definitions varied markedly in their sensitivity for identifying adverse opioid-related event, with low sensitivity overall across measures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.002 |
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 it