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Record W2330455436

Réduction des maladies cardiovasculaires et dépenses de santé au Québec à lhorizon 2050

2016· preprint· fr· W2330455436 on OpenAlexaboutno aff
David Boisclair, Yann Décarie, François Laliberté-Auger, Pierre‐Carl Michaud

Bibliographic record

VenueRePEc: Research Papers in Economics · 2016
Typepreprint
Languagefr
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesGeographyArt
DOInot available

Abstract

fetched live from OpenAlex

Les maladies cardiovasculaires (MCV), y compris l’hypertension et les accidents vasculaires cérébraux, comptent pour une part importante de la mortalité et des coûts directs de santé au Québec. Dans cette étude, nous utilisons un modèle de microsimulation afin de projeter jusqu’en 2050 les dépenses publiques en hospitalisations et en consultations médicales selon divers scénarios d’évolution de la présence des MCV. Un scénario plausible de baisse immédiate de 30% de l’incidence des MCV grâce à la prévention génère des économies cumulées de 21 milliards de dollars en valeur présente. À cela s’ajoute une valeur économique de 66 milliards de dollars de 2012 pour les années de vie sauvées au cours de la période.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.100
GPT teacher head0.390
Teacher spread0.290 · 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 designSimulation or modeling
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

Citations0
Published2016
Admission routes1
Has abstractyes

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