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Record W4285384827 · doi:10.2105/ajph.2022.306881

Xylazine and Overdoses: Trends, Concerns, and Recommendations

2022· article· en· W4285384827 on OpenAlexaff
Ryan S. Alexander, Bethany R. Canver, Kimberly L. Sue, Kenneth L. Morford

Bibliographic record

VenueAmerican Journal of Public Health · 2022
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsXylazineMedicinePublic healthHarmEnvironmental healthHarm reductionDrugSedativeDrug overdosePharmacologyIntensive care medicineAnesthesiaPoison controlKetaminePolitical scienceNursingLaw

Abstract

fetched live from OpenAlex

Xylazine is a nonopioid veterinary anesthetic and sedative that is increasingly detected in the illicit drug supply in the United States. Data indicate a striking prevalence of xylazine among opioid-involved overdose deaths. The emergence of xylazine in the illicit drug supply poses many unknowns and potential risks for people who use drugs. The public health system needs to respond by increasing testing to determine the prevalence of xylazine, identifying its potential toxicity at various exposure levels, and taking mitigating action to prevent harms. Currently, there is little testing capable of identifying xylazine in drug supplies, which limits the possibility of public health intervention, implementation of harm reduction strategies, or development of novel treatment strategies. (Am J Public Health. 2022;112(8):1212–1216. https://doi.org/10.2105/AJPH.2022.306881 )

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.004
Science and technology studies0.0020.001
Scholarly communication0.0050.009
Open science0.0030.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0120.005

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.

Opus teacher head0.071
GPT teacher head0.372
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations122
Published2022
Admission routes1
Has abstractyes

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