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Record W2137751744 · doi:10.1086/421949

Group A Streptococcal Pharyngitis Serotype Surveillance in North America, 2000–2002

2004· article· en· W2137751744 on OpenAlexaboutno aff
Stanford T. Shulman, Robert R. Tanz, William Kabat, Kathleen Kabat, Emily Cederlund, Devendra D. Patel, Zhongya Li, Varja Sakota, James B. Dale, Beall Bernard

Bibliographic record

VenueClinical Infectious Diseases · 2004
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPharyngitisSerotypeGenotypeMedicineVeterinary medicineDistribution (mathematics)Group ADemographyBiologyInternal medicineVirologyGenetics

Abstract

fetched live from OpenAlex

Geographic and interseasonal heterogeneity of pharyngeal group A streptococcal (GAS) genotypes (emm types) is poorly characterized. We evaluated emm type and subtype distribution among pediatric pharyngitis isolates obtained from 9 sites in the United States during 2000-2001 (year 1) and from 10 sites in the United States and 1 site in Canada during 2001-2002 (year 2). The 7 predominant types were the same in both years, although their order changed. emm 12, 1, and 28 accounted for 49.2% of year 1 isolates, and emm 1, 12, and 4 accounted for 47.1% of year 2 isolates; 6 types accounted for 72.1% in year 1 and 69.4% in year 2. From year 1 to year 2, the proportions of emm 12 and 28 decreased and emm 1 and 6 increased. Striking intersite and interseasonal variations in the distribution of predominant emm types were observed. We conclude that the most-predominant GAS genotypes were similar for each year despite fluctuations, that intersite and intrasite variations in the distribution of emm types were apparent, and that emm type surveillance is needed as M protein vaccine development proceeds.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.113
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.340
Teacher spread0.314 · 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

Citations128
Published2004
Admission routes1
Has abstractyes

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