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Record W2960603005 · doi:10.1128/jcm.00300-19

Molecular Diagnosis of Vaginitis: Comparing Quantitative PCR and Microbiome Profiling Approaches to Current Microscopy Scoring

2019· article· en· W2960603005 on OpenAlexafffund
Tarah Lynch, Gisele Peirano, Tracie Lloyd, Ron Read, Julia Carter, Ángel Chú, Jeffrey A. Shaman, Joseph P. Jarvis, Evan Diamond, Umer Zeeshan Ijaz, Deirdre L. Church

Bibliographic record

VenueJournal of Clinical Microbiology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsAlberta Health ServicesCalgary Laboratory ServicesUniversity of Calgary
FundersNatural Environment Research CouncilSight Research UKCalgary Laboratory Services
KeywordsVaginitisMicrobiomeProfiling (computer programming)BiologyComputational biologyMedicineMicrobiologyBioinformaticsComputer scienceGenetics

Abstract

fetched live from OpenAlex

Vaginitis is often diagnosed by microscopy and limited to testing for bacterial vaginosis (BV), vulvovaginal candidiasis, and trichomoniasis. Approximately 10% of vaginal swabs are negative but designated “altered flora” by BV Nugent score, leaving clinicians unsure how to treat patients. Accurate and comprehensive vaginitis diagnostics are needed to direct treatment and reduce risks of recurrent or more severe infections.

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)0.008
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.246
GPT teacher head0.433
Teacher spread0.186 · 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

Citations31
Published2019
Admission routes2
Has abstractyes

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