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Record W4401208164 · doi:10.1093/ntr/ntae111

Nicotine Toxicity From Repeat Use of Nicotine Pouches

2024· article· en· W4401208164 on OpenAlexaff
Jessica Kent, Garrick Mok, Emily Austin

Bibliographic record

VenueNicotine & Tobacco Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsNicotineMedicineConfusionNauseaToxicityAnesthesiaInternal medicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Nicotine pouches have emerged as a novel way to administer concentrated nicotine and come as a white powder in flavored, microfiber pouches placed between the cheek and gums to dissolve without requiring spitting. While marketed as a safe alternative to smoking, nicotine pouches have the potential for toxic exposure to users. CASE PRESENTATION: We present a case of a 21-year-old male with acute nicotine toxicity through repeated administration of nicotine pouches. Over the course of 12 hours, he consumed 15 extra-strength nicotine pouches (10.9 mg per pouch) as a study tool to prepare for the next-day exams. He presented to the emergency department with bizarre behavior requiring admission for persistent confusion and nausea which resolved after 24 hours. CONCLUSIONS: This case represents the first case of acute nicotine toxicity secondary to nicotine pouch use. These pouches are emerging as a novel way to use nicotine and present a serious risk of inadvertent overdose and harm. IMPLICATIONS: Nicotine pouches are emerging as a novel way to use nicotine, and second to e-cigarettes, are the most frequently used nicotine product among youth. These pouches, which lack clear warning labels, are promoted among social media forums and present a serious risk of inadvertent overdose and harm, especially among young adults. Healthcare professionals should be aware of this risk, especially from acute, repeated exposures, and should ensure the public is cautioned appropriately.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.202
GPT teacher head0.421
Teacher spread0.220 · 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 teacher head, not a consensus.

Study designObservational
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

Citations15
Published2024
Admission routes1
Has abstractyes

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