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Record W2167461153 · doi:10.1164/rccm.201501-0069oc

Fluoroquinolone Therapy for the Prevention of Multidrug-Resistant Tuberculosis in Contacts: A Cost-Effectiveness Analysis

2015· article· en· W2167461153 on OpenAlexaff
Gregory J. Fox, Olivia Oxlade, Dick Menzies

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineIncidence (geometry)TuberculosisInternal medicineIntensive care medicinePathology

Abstract

fetched live from OpenAlex

RATIONALE: Fluoroquinolone (FQN) therapy of latent tuberculosis infection among contacts of individuals with multidrug-resistant tuberculosis (MDR-TB) is controversial. OBJECTIVES: To determine the potential benefits, risks (including acquired FQN resistance), and cost-effectiveness of FQN therapy to prevent TB in contacts of individuals with MDR-TB. METHODS: We used decision analysis to estimate costs and outcomes associated with no therapy compared with a 6-month course of daily FQN therapy to treat latent TB infection in contacts of individuals with MDR-TB. Outcomes modeled were the incidence of MDR-TB, MDR-TB with FQN resistance, TB-related death, quality-adjusted life years, and health system costs. MEASUREMENTS AND MAIN RESULTS: FQN preventive therapy resulted in health system savings, lower incidence of MDR-TB, and lower mortality than no treatment. We found the incidence of MDR-TB with acquired FQN resistance would also be lower with FQN therapy of infected contacts. CONCLUSIONS: In our model, FQN preventive therapy resulted in substantial health system savings and in reduced mortality, incidence of MDR-TB, and incidence of acquired FQN-resistant disease as well as improved quality of life. FQN therapy remained cost saving with improved outcomes even if the effectiveness of therapy in preventing MDR-TB was as low as 10%.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.073
GPT teacher head0.420
Teacher spread0.347 · 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

Citations42
Published2015
Admission routes1
Has abstractyes

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