Naloxone interventions in opioid overdoses: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: North America is in the midst of an unabated opioid overdose epidemic due to the increasing non-medical use of fentanyl and ultra-potent opioids. Naloxone is an effective antidote to opioid toxicity, yet its optimal dosing in the context of fentanyl and ultra-potent opioid overdoses remains unknown. This review aims to determine the relationship between the first empiric dose of naloxone and reversal of toxicity, adverse events, and the total cumulative dose required among patients with undifferentiated opioid overdoses and those with suspected toxicity from ultra-potent opioids. Secondary objectives include evaluating the relationship between the cumulative naloxone dose and toxicity reversal and adverse events, among patients with undifferentiated opioid overdoses and those with suspected toxicity from ultra-potent opioids. METHODS: To identify studies, we will search MEDLINE, Embase, CENTRAL, DARE, CDAG, CINAHL, Science Citation Index, multiple trial registries, and the gray literature. Included studies will evaluate patients with suspected or confirmed opioid toxicity from undifferentiated opioids and ultra-potent opioids, who received an empiric and possibly additional doses of naloxone. The main outcomes of interest are the relationship between naloxone dose and toxicity reversal and adverse events. We will include controlled and non-controlled interventional studies, observational studies, case reports/series, and reports from poison control centers. We will extract data and assess study quality in duplicate with discrepancies resolved by consensus or a third party. We will use the Downs and Black and Cochrane risk of bias tools for observational and randomized controlled studies. If we find sufficient variation in dose, we will fit a random effects one-stage model to estimate a dose-response relationship. We will conduct multiple subgroup analyses, including by type of opioid used and by suspected high and low prevalence of ultra-potent opioid use based on geographic location and time of the original studies. DISCUSSION: Our review will include the most up-to-date available data including ultra-potent opioids to inform the current response to the opioid epidemic, addressing the limitations of recent reviews. We anticipate limitations relating to study heterogeneity. We will disseminate study results widely to update overdose treatment guidelines and naloxone dosing in Take Home Naloxone programs.
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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 | 0.039 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.012 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".