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Record W4205174892 · doi:10.7759/cureus.21045

Caffeine Intoxication: Unregulated, Over-the-Counter Sale of Potentially Deadly Supplements

2022· article· en· W4205174892 on OpenAlexaff
João Pina Cabral, David Sousa, Catarina Carvalho, Adriana Girao, Adriano Pacheco Mendes, Rui Pina

Bibliographic record

VenueCureus · 2022
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsCaffeineMedicineOver-the-counterAnesthesiaToxicologyIntensive care medicineMedical emergencyPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Caffeine is an integral part of beverages, food, and medications. Severe intoxication of caffeine is rare, and reports are even scarcer. However, over-the-counter, unregulated sale of performance-enhancing compounds, such as caffeine, turns high-dose consumption into a real concern. Severe intoxication may be fatal, usually by malignant cardiac arrhythmia. We report a case of a 23-year-old university student who accidentally consumed about 100 times the amount present in an expresso of anhydrous caffeine.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.020
GPT teacher head0.319
Teacher spread0.299 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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