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Record W2972906095 · doi:10.1039/c9tx00158a

Comment on “Acetylcysteine in paracetamol poisoning: a perspective of 45 years of use” by D. N. Bateman and J. W. Dear, Toxicol. Res., 2019, 8, 489

2019· article· en· W2972906095 on OpenAlexaffabout
Michael E. Mullins, Mark Yarema, Marco L.A. Sivilotti, Margaret Thompson, D. Adam Algren, Michael C. Beuhler, Christopher P. Holstege

Bibliographic record

VenueToxicology Research · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsHospital for Sick ChildrenQueen's UniversityAlberta Health Services
Fundersnot available
KeywordsAcetylcysteineProtocol (science)Perspective (graphical)MedicineChemistryComputer scienceArtificial intelligenceAlternative medicinePathologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract We point out an acetylcysteine protocol that a previous article (D. N. Bateman and J. W. Dear, Toxicol. Res., 2019, 8, 489–498) overlooked. The standard concentration protocol uses a uniform concentration of 30 mg mL−1 for all patients to reduce errors in preparation and administration. Usually a single 1 L bag is sufficient for most patients. Various centers in the US and Canada use this approach.

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)0.025
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: Editorial · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0030.006
Open science0.0040.002
Research integrity0.0430.046
Insufficient payload (model declined to judge)0.0050.008

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 head0.190
GPT teacher head0.489
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
GenreEditorial

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

Citations4
Published2019
Admission routes2
Has abstractyes

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