Olanzapine as the ideal “trip terminator”? Analysis of online reports relating to antipsychotics' use and misuse following occurrence of novel psychoactive substance‐related psychotic symptoms
Bibliographic record
Abstract
OBJECTIVE: The pharmacological self-management of novel psychoactive substance (NPS)-induced psychopathological consequences represents a fast growing phenomenon. This is facilitated by the frequent sharing of NPS intake experiences online and by the ease of access to a range of psychotropic medications from both the online and street market. Olanzapine is anecdotally reported by Web users to be the most frequent self-prescribed medication to cope with NPS-induced psychoses. Hence, we aimed here at better assessing olanzapine use/misuse for this purpose. METHODS: Exploratory qualitative searches of 163 discussion fora/specialized websites have been carried out in four languages (English, German, Spanish, and Italian) in the time frame November 2012-2013. RESULTS: Most NPS-users allegedly self administer with olanzapine to manage related psychotic crises/"bad trips". This may be typically taken only for a few days, at a dosage range of 5-50 mg/day. CONCLUSIONS: Only a few research studies have formally assessed the effectiveness of olanzapine and indeed of other second-generation antipsychotics to treat NPS-induced psychosis. Olanzapine was suggested here from a range of pro drug websites as being the "ideal" molecule to terminate "bad trips". Health professionals should be informed about the risks related to olanzapine misuse.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".