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Record W2177477149 · doi:10.1586/14787210.2016.1116385

Personalizing therapy for multidrug resistant TB: the potential of Rapid Whole Genome Sequencing

2015· editorial· en· W2177477149 on OpenAlexaff
Josephine M. Bryant, Marc Lipman, Judith Breuer

Bibliographic record

VenueExpert Review of Anti-infective Therapy · 2015
Typeeditorial
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsInstitute of Infection and Immunity
FundersSeventh Framework Programme
KeywordsWhole genome sequencingGenomeGold standard (test)TuberculosisMycobacterium tuberculosisComputational biologyMultiple drug resistanceBiologyDNA sequencingPersonal genomicsDrug resistanceGeneticsMedicineDNAGene

Abstract

fetched live from OpenAlex

Multidrug resistant tuberculosis is an increasing problem globally. The current gold standard in drug sensitivity testing is slow and cumbersome. To tackle drug resistance effectively, a more rapid method of testing is required. Current molecular tests are fast, but only offer information on a limited number of genetic loci. Whole genome sequencing presents an attractive alternative that can provide comprehensive, clinically relevant information on all described loci. Although the standard approach to whole genome sequencing of Mycobacterium tuberculosis is slow due to the requirement of culture, this article will describe recent advances that mean it has the potential to provide results within days.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.228
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.047
GPT teacher head0.389
Teacher spread0.343 · 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 designNot applicable
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

Citations5
Published2015
Admission routes1
Has abstractyes

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