Comparison of rates of opioid withdrawal symptoms and reversal of opioid toxicity in patients treated with two naloxone dosing regimens: a retrospective cohort study
Bibliographic record
Abstract
Introduction When managing opioid overdose (OD) patients, the optimal naloxone regimen should rapidly reverse respiratory depression while avoiding opioid withdrawal. Published naloxone administration guidelines have not been empirically validated and most were developed before fentanyl OD was common. In this study, rates of opioid withdrawal symptoms (OW) and reversal of opioid toxicity in patients treated with two naloxone dosing regimens were evaluated.Methods In this retrospective matched cohort study, health records of patients who experienced an opioid OD treated in two urban emergency departments (ED) during an ongoing fentanyl OD epidemic were reviewed. Definitions for OW and opioid reversal were developed a priori. Low dose naloxone (LDN; ≤0.15 mg) and high dose naloxone (HDN; >0.15 mg) patients were matched in a 1:4 ratio based upon initial respiratory rate (RR). The proportion of patients who developed OW and who met reversal criteria were compared between those treated initially with LDN or HDN. Odds ratios (OR) for OW and opioid reversal were obtained via logistic regression stratified by matched sets and adjusted for age, sex, pre-naloxone GCS, and presence of non-opioid drugs or alcohol.Results Eighty LDN patients were matched with 299 HDN patients. After adjustment, HDN patients were more likely than LDN patients to have OW after initial dose (OR = 8.43; 95%CI: 1.96, 36.3; p = 0.004) and after any dose (OR = 2.56; 95%CI: 1.17, 5.60; p = 0.019). HDN patients were more likely to meet reversal criteria after initial dose (OR = 2.73; 95%CI: 1.19, 6.26; p = 0.018) and after any dose (OR = 6.07; 95%CI: 1.81, 20.3; p = 0.003).Conclusions HDN patients were more likely to have OW but also more likely to meet reversal criteria versus LDN patients.
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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 | 0.001 |
| 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) | 0.002 | 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".