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Record W2980361713 · doi:10.1128/jcm.01302-19

Recovery of Nontuberculous Mycobacteria and Nocardiae Causing Skin/Soft Tissue Infections by Use of the Copan ESwab Collection and Transport System

2019· letter· en· W2980361713 on OpenAlexaff
B. Suresh Gandhi, Gail L. Woods, Tony Mazzulli

Bibliographic record

VenueJournal of Clinical Microbiology · 2019
Typeletter
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsNontuberculous mycobacteriaSoft tissueMycobacteriumMicrobiologyMycobacterium InfectionsMedicineBiologyPathologyTuberculosis

Abstract

fetched live from OpenAlex

Recent reports have shown that infections with nontuberculous mycobacteria (NTM) are increasing worldwide and often are difficult to diagnose and to treat ([1][1][–][2][3][3]). Consequently, improved diagnostics are needed. Clinical and Laboratory Standards Institute (CLSI) guidelines ([4][4])

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.005

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 head0.030
GPT teacher head0.319
Teacher spread0.289 · 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.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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