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Record W2262358356 · doi:10.7019/tpj.200912.0107

Prevention and Monitoring of Hepatotoxicity among Patients Receiving Antituberculosis Medications

2009· article· en· W2262358356 on OpenAlexaff
Fei‐Yuan Hsiao, Yu Hsuan Yen, Chun-Nin Lee, Weng F. Huang, Hsiang‐Yin Chen

Bibliographic record

VenueZhōnghuá yàoxué zázhì · 2009
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMedicineInternal medicineAlanine aminotransferaseLogistic regressionLiver functionLiver function testsGastroenterology

Abstract

fetched live from OpenAlex

Antituberculosis therapy frequently causes hepatotoxicity. The study is to evaluate the appropriateness of liver function monitoring during antituberculosis therapy. Two hundred forty five patients treated with antituberculosis agents were included. Abnormal baseline liver function (LFT) was the most significant risk factor for developing hepatotoxicity during the therapy (adjusted OR 23.48; 95% CI: 9.74-56.61). However, the baseline aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels were only checked in 76.2% and 75.4% subjects in the hepatotoxic group; and even lower to 58.5% and 57.8% for the non-hepatotoxic group. Although smoking, severe drinking, age, gender and concurrent diseases were significant risk factors, the logistic regression showed that only abnormal baseline LFT (adjusted OR 2.21; 95% CI: 1.22-4.02) and age (adjusted OR 1.02; 95% CI: 1.01-1.04) were determinants of patients receiving follow-up liver function tests (LFTs). Effective strategies to improve the monitoring of liver function should be established to ensure patient safety.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.121
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.077
GPT teacher head0.406
Teacher spread0.329 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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