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Record W2133278485 · doi:10.1086/345833

A Pharmacoeconomic Evaluation of 7‐Valent Pneumococcal Conjugate Vaccine in Canada

2003· article· en· W2133278485 on OpenAlexaffabout
Marc Lebel, James D. Kellner, E. Lee Ford‐Jones, Kyle Hvidsten, Edward C. Y. Wang, Vincent Ciuryla, Steve Arikian, Roman Casciano

Bibliographic record

VenueClinical Infectious Diseases · 2003
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsHospital for Sick ChildrenSickKids FoundationUniversity of TorontoUniversity of CalgaryAlberta Children's HospitalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicinePneumococcal conjugate vaccinePneumococcal infectionsConjugateStreptococcus pneumoniaeIntensive care medicineMicrobiologyAntibiotics

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate the projected health benefits, costs, and cost-effectiveness of pneumococcal conjugate vaccination for infants and children aged <5 years in Canada. A health state model incorporating incidence, vaccine efficacy, costs, and transitional probabilities for the health states (well, meningitis, bacteremia, otitis media, pneumonia, and death) was constructed for a 10-year time horizon. Implementation of a pneumococcal conjugate vaccine program in Canada for each annual birth cohort of 340,000 persons observed over 10 years would be expected to save approximately 12 lives and 100,000 cases of pneumococcal disease over 10 years, resulting in total savings of $67 million (Canadian dollars [Can$]). Vaccination of healthy infants would result in net savings for society if the vaccine costs less than Can$50 per dose. Moreover, for a vaccine purchase price of Can$67.50, infant vaccination would cost society Can$79,000 per life-year gained. Pneumococcal conjugate vaccination is a potentially cost-effective means of pneumococcal disease prevention.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.383
Teacher spread0.331 · 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

Citations52
Published2003
Admission routes2
Has abstractyes

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