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Record W2107898469 · doi:10.1111/hdi.12293

Isoniazid poisoning: Pharmacokinetics and effect of hemodialysis in a massive ingestion

2015· article· en· W2107898469 on OpenAlexvenueno aff
Kirsty Skinner, Ana Saiao, Ahmed Mostafa, Jessamine Soderstrom, Gregory Medley, Michael S. Roberts, Geoffrey K. Isbister

Bibliographic record

VenueHemodialysis International · 2015
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsnot available
FundersNational Health and Medical Research Council
KeywordsMedicinePharmacokineticsIsoniazidHemodialysisIngestionAnesthesiaMidazolamSedationPharmacologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Isoniazid is a rare overdose that causes seizures and there is limited evidence to guide treatment. We report a 20-year-old female migrant who presented with recurrent seizures after ingesting 25 g of isoniazid. She was treated with activated charcoal, repeated doses of midazolam for the seizures, and given multiple doses of pyridoxine (14 mg), limited by availability. She was admitted to intensive care, and 5.5 hours post-ingestion, she was commenced on continuous veno-venous hemodiafiltration (CVVHDF). She was extubated after 24 hours and CVVHDF was ceased 6 hours later (30 hours post-overdose). Her renal function remained normal and her initial lactate was the highest at 2.3. She made a full recovery. Five plasma samples were collected before, during, and after CVVHDF, and isoniazid was quantified with liquid chromatography-tandem mass spectrometry. A pharmacokinetic analysis of time-isoniazid concentration data was fitted to a two-compartment model with first-order input (with fixed ka ) with the effect of CVVHDF modeled as a time-dependent covariate. This suggested that there was initially good clearance with CVVHDF (4 times endogenous clearance), which rapidly declined within hours.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.313
Teacher spread0.294 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2015
Admission routes1
Has abstractyes

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