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Record W4417343835 · doi:10.1002/bcp.70387

Drug poisonings following bariatric surgery: Case series report

2025· article· en· W4417343835 on OpenAlexaff
Eman Mshari, Fannie Lajeunesse‐Trempe, Stephen R. Morley, Caroline S. Copeland

Bibliographic record

VenueBritish Journal of Clinical Pharmacology · 2025
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedical prescriptionDrugPopulationPrescription drugVulnerability (computing)MEDLINEPoison controlInjury prevention

Abstract

fetched live from OpenAlex

Bariatric surgery alters gastrointestinal anatomy and physiology, which likely impacts upon oral medication absorption. Drug- and alcohol-related deaths in this population are being increasingly reported; however, toxicological detail is lacking. Using data reported to the National Programme on Substance Use Mortality, we identified 18 deaths in people who had previously undergone bariatric surgery. Opioids were detected in almost all cases and were frequently implicated in causing death. Multiple medications were detected at post-mortem in every case and often included medications that the deceased was not actively prescribed. Mental health conditions and chronic pain were commonly listed comorbidities and one-third of deaths were deemed suicide. Illicit drug and alcohol use was rare, highlighting a distinct vulnerability to prescription medication harms. Our findings emphasize a need to understand how bariatric surgery alters pharmacokinetics and for integrated, multidisciplinary aftercare, therapeutic drug monitoring and tailored education of safe medication use in bariatric surgery patients.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0050.002

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 head0.038
GPT teacher head0.403
Teacher spread0.365 · 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 designCase report
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

Citations0
Published2025
Admission routes1
Has abstractyes

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