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Record W1998716311 · doi:10.1186/1471-2431-14-189

Direct Streptococcus pneumoniae real-time PCR serotyping from pediatric parapneumonic effusions

2014· article· en· W1998716311 on OpenAlexaffabout
Robert Slinger, Lucie Hyde, Ioana Moldovan, Francis Chan, Jeffrey M. Pernica

Bibliographic record

VenueBMC Pediatrics · 2014
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsMcMaster UniversityChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersPfizer
KeywordsSerotypeStreptococcus pneumoniaeMedicinePneumoniaMicrobiologyVirologyPneumococcal vaccinePolymerase chain reactionBiologyInternal medicineAntibioticsGene

Abstract

fetched live from OpenAlex

BACKGROUND: To determine the serotypes of Streptococcus pneumoniae responsible for pneumonia complicated by parapneumonic effusion in children, we performed real-time PCR based pneumococcal "serotyping" directly on parapneumonic fluid samples. METHODS: Specimens were collected at two children's hospitals in Ontario, Canada from 2009 to 2011. Samples in which S. pneumoniae was detected by PCR were tested with serotype-specific 5'exonuclease PCR assays for the 13 serotypes contained in the 13-serotype pneumococcal vaccine. RESULTS: Thirty-five S. pneumoniae PCR-positive pleural samples were studied. Pneumococcal serotyping PCR assays were positive for 34 of 35 (97%). Serotype 3 was detected most frequently, in 19/35 (54%), followed by serotype 19A in 9/35 (26%), serotype 7 F/A in 4/35 (11%), serotype 1 in 1/35 (3%), and serotype 6A also in 1/35 (3%). CONCLUSIONS: PCR testing demonstrated that the vast majority (97%) of S. pneumoniae parapneumonic effusions were caused by serotypes present in the 13-serotype vaccine that were not present in the original 7 serotype vaccine. This suggests that use of the 13-serotype vaccine could potentially prevent many S. pneumoniae pneumonias complicated by parapneumonic effusion in our region, provided serotype replacement does not occur.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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.

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

Citations20
Published2014
Admission routes2
Has abstractyes

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