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Record W1985325758 · doi:10.1086/313923

Invasive Pneumococcal Infections in Canadian Children, 1991-1998: Implications for New Vaccination Strategies

2000· article· en· W1985325758 on OpenAlexaffabout
David W. Scheifele, Scott A. Halperin, L Pelletier, James Talbot

Bibliographic record

VenueClinical Infectious Diseases · 2000
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsProvincial Laboratory of Public HealthHealth CanadaCanadian Paediatric Society
Fundersnot available
KeywordsMedicineSerotypeVaccinationConjugate vaccinePneumococcal infectionsPenicillinStreptococcus pneumoniaePneumococcal conjugate vaccinePediatricsMeningitisImmunizationAntibioticsVirologyImmunologyMicrobiologyBiologyAntigen

Abstract

fetched live from OpenAlex

We reviewed 2040 consecutive cases of invasive pneumococcal infection that were seen at 11 pediatric centers across Canada during 1991-1998 to determine if such infections could be prevented by new conjugate vaccines. Isolates from 1528 cases were serotyped. Most cases (61.5%) occurred in patients aged >2 years. Underlying medical conditions were present in 23.2% of case patients. Serotypes in the 7-valent conjugate vaccine matched isolates as follows: 85.8% of tested isolates from children aged 6 months to 5 years, but significantly fewer isolates in younger and older children; 72.9% of isolates from non-healthy children, but 83.9% of isolates from previously healthy children; and 95.4% of isolates with high-level penicillin resistance, but only 72.7% of those with intermediate-level resistance. Significant natural variation in the proportion of isolates matching 7-valent vaccines occurred from year to year and among centers. New conjugate vaccines have great potential but their effectiveness and limitations require ongoing study.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.024
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.367
Teacher spread0.336 · 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

Citations88
Published2000
Admission routes2
Has abstractyes

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