MétaCan
Menu
Back to cohort
Record W2082403900 · doi:10.3201/eid2006.131069

Oral Fluid Testing for Pertussis, England and Wales, June 2007–August 2009

2014· article· en· W2082403900 on OpenAlexaff
Helen Campbell, Gayatri Amirthalingam, Norman K. Fry, David Litt, Timothy G. Harrison, Karen Wagner, Natasha S. Crowcroft, Elizabeth Miller

Bibliographic record

VenueEmerging infectious diseases · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsPublic Health Ontario
FundersPublic Health England
KeywordsMedicineEpidemiologyPediatricsWhooping coughVaccinationInternal medicineImmunology

Abstract

fetched live from OpenAlex

Existing pertussis surveillance systems tend to underidentify less severe cases among older children and adults. For routine follow-up of notified, nonconfirmed, clinically diagnosed pertussis cases, use of an oral fluid test was pilot tested in England and Wales during June 2007-August 2009. During that period, 1,852 cases of pertussis were confirmed by established laboratory methods and another 591 by oral fluid testing only. Although introduction of serologic testing in 2002 led to the greatest increase in ascertainment of pertussis, oral fluid testing increased laboratory ascertainment by 32% overall; maximal increase (124%) occurred among children 5-9 years of age. Patients whose pertussis was confirmed by oral fluid testing were least likely to be hospitalized, suggesting that milder community cases were being confirmed by this method. Oral fluid testing is an easily administered, noninvasive surveillance tool that could further our understanding of pertussis epidemiology and thereby contribute to decisions on vaccination strategies.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations25
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueEmerging infectious diseasesSame topicBacterial Infections and VaccinesFrench-language works237,207