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Record W2075098875 · doi:10.5588/ijtld.12.0279

Therapeutic drug monitoring in the treatment of tuberculosis: a retrospective analysis

2013· article· en· W2075098875 on OpenAlexaffabout
Lindsay Van Tongeren, Victoria Cook, J. Mark FitzGerald, James C. Johnston

Bibliographic record

VenueThe International Journal of Tuberculosis and Lung Disease · 2013
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePyrazinamideRifampicinTherapeutic drug monitoringTuberculosisDrugRetrospective cohort studyIsoniazidInternal medicineDrug resistancePopulationBedaquilineSurgeryMycobacterium tuberculosisPharmacologyPathology

Abstract

fetched live from OpenAlex

SETTING: Tuberculosis (TB) in-patient treatment unit in Vancouver, Canada. OBJECTIVE: To examine the results of therapeutic drug monitoring (TDM) in anti-tuberculosis treatment. DESIGN: We performed a retrospective analysis of TDM data from 2000 to 2010. All in-patients treated for TB with TDM performed during their treatment course were included. RESULTS: TDM was performed on 52 patients in 76 treatment episodes from 2000 to 2010. Overall, 103/213 (48.4%) drug levels measured were low, and 5/213 (2.3%) were high. At least one drug level was low in 47/52 (90.3%) patients. Initial serum levels were low in respectively 76.6% and 68.4% of isoniazid (INH) and rifampicin (RMP) levels. In contrast, only 2.9% of initial pyrazinamide levels were low. Five patients with a susceptible strain on initial presentation later developed drug-resistant disease, with all five patients demonstrating at least one low drug level and two demonstrating multiple low levels. Dose adjustments were made in response to 26 INH and RMP levels, with variable serum responses. CONCLUSION: In this population with high rates of treatment failure and acquired resistance, we demonstrate that most patients had low drug levels. Prospective studies are required to examine the relationship between drug levels and clinical outcomes.

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 score0.039
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.018
GPT teacher head0.332
Teacher spread0.314 · 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

Citations40
Published2013
Admission routes2
Has abstractyes

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