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Record W2013526236 · doi:10.1016/s2213-2600(13)70254-6

Molecular methods for tuberculosis trials: time for whole-genome sequencing?

2013· letter· en· W2013526236 on OpenAlexaff
Dick Menzies

Bibliographic record

VenueThe Lancet Respiratory Medicine · 2013
Typeletter
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineWhole genome sequencingTuberculosisComputational biologyGenomeMEDLINEGeneticsGenePathologyBiology

Abstract

fetched live from OpenAlex

The first genetic sequencing of Mycobacterium tuberculosis was a momentous achievement that required years of painstaking effort and substantial funding.1 It is remarkable that 15 years later, advances in laboratory techniques and informatics have enabled whole-genome sequencing to be done for hundreds of M tuberculosis isolates, making this information available for epidemiological studies.2,3 The increasing availability of whole-genome sequencing has raised questions about the interpretation of this new method,3 as well as its public health and clinical applications and utility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.387
metaresearch head score (Gemma)0.538
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.387
Threshold uncertainty score0.757

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3870.538
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0120.008
Bibliometrics0.0060.009
Science and technology studies0.0030.014
Scholarly communication0.0210.033
Open science0.0070.012
Research integrity0.0180.034
Insufficient payload (model declined to judge)0.0640.030

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 head0.220
GPT teacher head0.463
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2013
Admission routes1
Has abstractyes

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