Early Discharge of Patients with Presumed Opioid Overdose: Development of a Clinical Prediction Rule
Bibliographic record
Abstract
OBJECTIVE: To develop a clinical prediction rule to identify patients who can be safely discharged one hour after the administration of naloxone for presumed opioid overdose. METHODS: Patients who received naloxone for known or presumed opioid overdose were formally evaluated one hour later for multiple potential predictor variables. Patients were classified into two groups: those with adverse events within 24 hours and those without. Using classification and regression tree methodology, a decision rule was developed to predict safe discharge. RESULTS: Clinical findings from 573 patients allowed us to develop a clinical prediction rule with a sensitivity of 99% (95% CI = 96% to 100%) and a specificity of 40% (95% CI = 36% to 45%). Patients with presumed opioid overdose can be safely discharged one hour after naloxone administration if they: 1) can mobilize as usual; 2) have oxygen saturation on room air of >92%; 3) have a respiratory rate >10 breaths/min and <20 breaths/min; 4) have a temperature of >35.0 degrees C and <37.5 degrees C; 5) have a heart rate >50 beats/min and <100 beats/min; and 6) have a Glasgow Coma Scale score of 15. CONCLUSIONS: This prediction rule for safe early discharge of patients with presumed opioid overdose performs well in this derivation set but requires validation followed by confirmation of safe implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".