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Record W4385699997 · doi:10.51731/cjht.2023.711

Cost-Effectiveness of an RSVpreF Vaccine for Prevention of Respiratory Syncytial Virus Outcomes in Infants

2023· article· en· W4385699997 on OpenAlexaboutno aff
Samantha Verbrugghe, Quenby Mahood, Sean Tiggelaar

Bibliographic record

VenueCanadian Journal of Health Technologies · 2023
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsnot available
Fundersnot available
KeywordsImmunizationMedicinePregnancyCost effectivenessPediatricsImmunologyAntibodyBiology

Abstract

fetched live from OpenAlex

From CADTH’s search of the economic literature, 4 economic studies were identified that assessed the cost-effectiveness of respiratory syncytial virus (RSV) immunization during pregnancy in high-income countries, including 1 study set in Nunavik, Quebec. Only 1 of these studies specifically considered the product of interest (RSVpreF), and only in a scenario analysis. In the 4 identified studies that evaluated the cost-effectiveness of RSV immunization during pregnancy, the outcomes predicted by the models focused on those related to infants. There is a lack of evidence on outcomes — thus cost-effectiveness — for the persons who are pregnant. The results from the 4 studies varied considerably. RSV immunization during pregnancy ranged from being more effective and associated with lower total costs (dominant) to more than $200,000 per quality-adjusted life-year gained when compared with no intervention. The results depended on the modelled region, efficacy, pricing, and severity of the RSV season. In 2 studies, year-round RSV immunization during pregnancy was not considered cost-effective compared with seasonal RSV prophylaxis with long-acting monoclonal antibodies (mAbs), such as nirsevimab, when the price per dose was the same as that of the long-acting mAb. RSV immunization during pregnancy was estimated to become cost-effective when its acquisition cost per dose was 2 to 5 times lower than that of the long-acting mAb.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.583
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
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 head0.169
GPT teacher head0.471
Teacher spread0.302 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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