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Record W4416225504 · doi:10.1080/14760584.2025.2589214

Off-Label use of vaccines may save lives and money: lessons from the province of Quebec, Canada

2025· article· en· W4416225504 on OpenAlexafffundabout
Philippe De Wals, Caroline Quach

Bibliographic record

VenueExpert Review of Vaccines · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité LavalInstitut National de Santé Publique du QuébecUniversité de MontréalUniversité de Sherbrooke
FundersInstitut National de Santé Publique du QuébecUniversité Laval
KeywordsInterchangeabilityImmunizationLimitingVaccinationRegimenRoutine immunizationScientific evidence

Abstract

fetched live from OpenAlex

INTRODUCTION: In Canada, vaccines are authorized by Health Canada but publicly funded programs are of provincial/territorial jurisdiction. Off-label (OL) use of vaccines has been frequently implemented in Quebec over the last 30 years. AREAS COVERED: The first part of this manuscript describes 11 recommendations from the Quebec Immunization Committee on meningococcal, pneumococcal, hepatitis A and B, HPV, rotavirus, and COVID-19 vaccines that were clearly OL. In the second part, challenges associated with OL use are discussed, including (i) the justifications of OL recommendations, (ii) the level of supporting scientific evidence, (iii) effectiveness and safety considerations, (iv) vaccine confidence and acceptability, (v) liability risks and informed consent. EXPERT OPINION: With one exception, OL vaccine use in Quebec was successful. Reducing the number of doses or recommending the use of two different vaccines in a single immunization regimen (one vaccine having a much lower purchase cost than the other) allowed for more cost-effective immunization programs. Another OL's justification was to increase protection, by extending the age limit or interval between doses, or allowing an interchangeability of available vaccines. OL vaccine use should always be considered when properly justified by scientific evidence and vaccinology principles, and carefully evaluated when implemented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.225
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.331
Teacher spread0.303 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes3
Has abstractyes

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