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Record W3213545386 · doi:10.1051/e3sconf/202131901042

Risk Management Assessments of Anti-tuberculosis Adverse Drug Reaction: A Systematic Review

2021· review· en· W3213545386 on OpenAlexaboutno aff
Boubacar Traoré, Gladys Tsoumbou Bakana, Samira Nani, Samira Hassoune

Bibliographic record

VenueE3S Web of Conferences · 2021
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiscontinuationAdverse effectMedical prescriptionIntensive care medicineTuberculosisMEDLINESystematic reviewPharmacologyInternal medicinePathology

Abstract

fetched live from OpenAlex

To improve adherence to treatment, quality of life of patients on anti-tuberculosis drugs, and prevent antibiotic resistance, we conducted this systematic review to support risk minimization actions. Methods: Medline, Scopus, and Web of Science were searched with a focus on adverse drug reactions. Two independent reviewers assessed the methodological quality of the included studies using criteria defined by the Newcastle Ottawa Scale. Results: Seven studies were included, and four risk management strategies were identified (psychological intervention, drug dose reduction with or without prescription of adjunctive drugs, drug switching, permanent or temporary drug discontinuation). The strategies adopted were dependent on the nature and severity of the adverse events. All drugs responsible for serious adverse effects were changed or discontinued. Conclusions: The results highlight the relatively low frequency of adverse events leading to permanent discontinuation of 1st-line anti-tuberculosis drugs, but also emphasize the high incidence of adverse events leading to permanent discontinuation of cycloserine.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.135
GPT teacher head0.484
Teacher spread0.348 · 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.

Study designSystematic review
Domainnot available
GenreReview

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
Published2021
Admission routes1
Has abstractyes

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