New Opioid Use after Invasive Mechanical Ventilation and Hospital Discharge
Bibliographic record
Abstract
Abstract Rationale Patients who receive invasive mechanical ventilation (IMV) are usually exposed to opioids as part of their sedation regimen. The rates of posthospital prescribing of opioids are unknown. Objectives To determine the frequency of persistent posthospital opioid use among patients who received IMV. Methods We assessed opioid-naive adults who were admitted to an ICU, received IMV, and survived at least 7 days after hospital discharge in Ontario, Canada over a 26-month period (February, 2013 through March, 2015). The primary outcome was new, persistent opioid use during the year after discharge. We assessed factors associated with persistent use by multivariable logistic regression. Patients receiving IMV were also compared with matched hospitalized patients who did not receive intensive care (non-ICU). Measurements and Main Results Among 25,085 opioid-naive patients on IMV, 5,007 (20.0%; 95% confidence interval [CI], 19.5–20.5) filled a prescription for opioids in the 7 days after hospital discharge. During the next year, 648 (2.6%; 95% CI, 2.4–2.8) of the IMV cohort met criteria for new, persistent opioid use. The patient characteristic most strongly associated with persistent use in the IMV cohort was being a surgical (vs. medical) patient (adjusted odds ratio, 3.29; 95% CI, 2.72–3.97). The rate of persistent use was slightly higher than for matched non-ICU patients (2.6% vs. 1.5%; adjusted odds ratio, 1.37 [95% CI, 1.19–1.58]). Conclusions A total of 20% of IMV patients received a prescription for opioids after hospital discharge, and 2.6% met criteria for persistent use, an average of 300 new persistent users per year in a population of 14 million. Receipt of surgery was the factor most strongly associated with persistent 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 | 0.012 |
| 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 | 0.001 |
| 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".