Case Study: Naltrexone for the Treatment of Nitrous Oxide Use
Bibliographic record
Abstract
BACKGROUND: Using a clinical case example, we describe and discuss the use of oral naltrexone as a novel treatment strategy for nitrous oxide use. Nitrous oxide is an inhalant drug that is readily available and legally obtained. Though frequency of reported cases of substance use disorder for nitrous oxide is low, previous case reports have described severe neurological and psychiatric harms associated with chronic use. Despite this, evidence for pharmacotherapy is currently lacking. Clinical studies have shown variable efficacy for naltrexone across a number of substances including alcohol, nicotine, and stimulants. CASE: We present here a case of a 41-year-old man with a substance use disorder for nitrous oxide who was reportedly using of up to four hundred 8 g canisters of nitrous oxide per day. Oral naltrexone was initiated at 50 mg daily in an attempt to decrease cravings. The dose was subsequently titrated to 100 mg daily, resulting in a decrease in nitrous oxide use to less than sixty 8 g canisters per week over a 1-month timeframe. DISCUSSION: Previous literature surrounding naltrexone provides both a plausible mechanism of action for craving reduction as well as a precedent for its use across a number of substances. To our knowledge, use of naltrexone for nitrous oxide use has not been previously described. While clinical studies are currently lacking, this case highlights naltrexone as a possible treatment strategy for nitrous oxide use, with potential to reduce significant harms associated with chronic use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".