Persistent Opioid Use After Cardiac Implantable Electronic Device Procedures
Bibliographic record
Abstract
BACKGROUND: Prescription opioids are a major contributor to the ongoing epidemic of persistent opioid use (POU). The incidence of POU among opioid-naïve patients after cardiac implantable electronic device (CIED) procedures is unknown. METHODS: This retrospective cohort study used data from a national administrative claims database from 2004 to 2018 of patients undergoing CIED procedures. Adult patients were included if they were opioid-naïve during the 180-day period before the procedure and did not undergo another procedure with anesthesia in the next 180 days. POU was defined by filling an additional opioid prescription >30 days after the CIED procedure. RESULTS: =0.5). In a sensitivity analysis excluding high-risk patients who were discharged to a facility or who had a history of drug abuse or previous opioid, benzodiazepine, or muscle relaxant use, 8.9% of the remaining cohort had POU. Patients prescribed >135 mg of oral morphine equivalents had a significantly increased risk of POU. CONCLUSIONS: POU is common after CIED procedures, and 12% of patients continued to use opioids >30 days after surgery. Higher initially prescribed oral morphine equivalent doses were associated with developing POU.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".