The effects of vaped cannabis on the severity of naloxone-precipitated opioid withdrawal.
Bibliographic record
Abstract
50) withdrawal using the Clinical Opiate Withdrawal Scale (COWS, range = 0-48) as the primary dependent measure. Evaluating the safety of this drug combination was the secondary aim, assessed using vital signs. Before a major methodological redesign, a single participant (male, 52) with opioid use disorder completed testing. The ∼4-week inpatient study began with stabilization on oral morphine (120 mg/day). During testing, the following dose combinations of vaped cannabis (V-CB) and intranasal naloxone (IN-NLX) were tested: (a) IN-NLX 0.0 mg + V-CB 25.0 mg, (b) IN-NLX 4.0 mg + V-CB 0.0 mg, (c) IN-NLX 0.0 mg + V-CB 12.5 mg, (d) IN-NLX 4.0 mg + V-CB 12.5 mg, (e) IN-NLX 0.0 mg + V-CB 0.0 mg, and (f) IN-NLX 4.0 mg + V-CB 25.0 mg. Naloxone alone resulted in a COWS score of 22 at T+30. CB pretreatment (12.5 mg and 25.0 mg) reduced COWS scores at T+30 to 17 and 14, respectively. Active NLX and V-CB administered in combination resulted in elevated heart rate and blood pressure, though not to a greater extent than NLX alone. This study found that the addition of a cannabinoid reduced the severity of NLX-precipitated withdrawal and supported the continued investigation into combined NLX + cannabinoid formulations as overdose reversal agents. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".