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Record W2761144537 · doi:10.3201/eid2311.170077

Polyclonal Pulmonary Tuberculosis Infections and Risk for Multidrug Resistance, Lima, Peru

2017· article· en· W2761144537 on OpenAlexfundno aff
Ruvandhi R. Nathavitharana, Cynthia X. Shi, Leonid Chindelevitch, Róger Calderón, Zibiao Zhang, Jerome T. Galea, Carmen Contreras, Rosa Yataco, Leonid Lecca, Mercedes C. Becerra, Megan Murray, Ted Cohen

Bibliographic record

VenueEmerging infectious diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of General Medical SciencesNational Institute of Mental HealthNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthImperial College LondonWellcome TrustCenter for AIDS Research, University of WashingtonAlfred P. Sloan Foundation
KeywordsTuberculosisMycobacterium tuberculosisPulmonary tuberculosisMultiple drug resistanceMedicineImmunologyPolyclonal antibodiesMicrobiologyBiologyDrug resistancePathologyAntibody

Abstract

fetched live from OpenAlex

Because within-host Mycobacterium tuberculosis diversity complicates diagnosis and treatment of tuberculosis (TB), we measured diversity prevalence and associated factors among 3,098 pulmonary TB patients in Lima, Peru. The 161 patients with polyclonal infection were more likely than the 115 with clonal or the 2,822 with simple infections to have multidrug-resistant TB.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.015
GPT teacher head0.324
Teacher spread0.309 · 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 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

Citations13
Published2017
Admission routes1
Has abstractyes

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