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Record W3046094389 · doi:10.1128/msphere.00478-20

The Challenges of Using Oropharyngeal Samples To Measure Pneumococcal Carriage in Adults

2020· article· en· W3046094389 on OpenAlexfundno aff
Laura K. Boelsen, Eileen M. Dunne, Katherine A. Gould, F. Tupou Ratu, Jorge E. Vidal, Fiona M. Russell, Kim Mulholland, Jason Hinds, Catherine Satzke

Bibliographic record

VenuemSphere · 2020
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilNational Institutes of HealthDepartment of Foreign Affairs and Trade, Australian GovernmentState Government of VictoriaPfizerAustralian GovernmentSanofi PasteurMurdoch Children's Research InstituteVeskiUniversity of MelbourneInstitute of Infection and ImmunityGlaxoSmithKlineBill and Melinda Gates FoundationEmory UniversityNational Institute of Allergy and Infectious DiseasesChildren’s Hospital of Wisconsin Research InstituteSanofi
KeywordsStreptococcus pneumoniaeSerotypeMultiplex polymerase chain reactionMicrobiologyLatex fixation testMultiplexBiologyPolymerase chain reactionAntibodyImmunologyGeneBioinformaticsGeneticsAntibiotics

Abstract

fetched live from OpenAlex

(the pneumococcus) is a significant global pathogen. Accurate identification and serotyping are vital. In contrast with World Health Organization recommendations based on culture methods, we demonstrate that pneumococcal identification and serotyping with molecular methods are affected by sample type. Results from oropharyngeal samples from adults were often inaccurate. This is particularly important for assessment of vaccine impact using carriage studies, particularly in low- and middle-income countries where there are significant barriers for disease surveillance.

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)0.045
metaresearch head score (Gemma)0.083
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 score0.045
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.067
GPT teacher head0.290
Teacher spread0.223 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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