Is Admission to the Intensive Care Unit Associated With Chronic Opioid Use? A 4-Year Follow-Up of Intensive Care Unit Survivors
Bibliographic record
Abstract
PURPOSE: To describe opioid use before and after intensive care unit (ICU) admission and to identify factors associated with chronic opioid use upto 4 years after ICU discharge. METHODS: Retrospective review of adult patients admitted to the ICU at a tertiary care center between January 1, 2005, to December 31, 2008. We defined "nonuser," "intermittent," and "chronic" opioid status by abstinence, use in <70%, and >70% of days for a given time period, respectively. We assessed opioid use at 3 months prior to ICU admission, at discharge, and annually for upto 4 years following ICU discharge. RESULTS: A total of 2595 ICU patients were included for surgical (48.6%), medical (38.4%), and undetermined (13%) indications. The study population included both elective (26.9%) and emergent (73.1%) admissions. Three months prior to ICU admission, 76.9% were nonusers, 16.9% used opioids intermittently, and 6.2% used opioids chronically. We found an increase in nonuser patients from 87.8% in the early post-ICU period to 95.6% at 48-month follow-up. Consequently, intermittent and chronic opioid use dropped to 8.6% and 3.6% at discharge and 2.6% and 1.8% at 48-month follow-up, respectively. Prolonged hospital length of stay was associated with chronic opioid use. CONCLUSION: Admission to ICU and duration of ICU stay were not associated with chronic opioid use.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".