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Record W953856711

Abstract 17199: Cost-Effectiveness of Fondaparinux versus Enoxaparin in Non-st-Elevation Acute Coronary Syndrome in Canada. Oasis-5

2010· article· en· W953856711 on OpenAlexaboutno aff
Greta Lozano‐Ortega, Robyn Kendall, Mark Sculpher, Jorge Ross Terrés, Reid Robson, Adrian R. Levy

Bibliographic record

VenueCirculation · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAcute coronary syndromeFondaparinuxMyocardial infarctionEmergency medicineClinical trialCost effectivenessST elevationEnoxaparin sodiumLow molecular weight heparinInternal medicineHeparinVenous thromboembolism
DOInot available

Abstract

fetched live from OpenAlex

Purpose: To estimate the lifetime cost-effectiveness of fondaparinux compared to enoxaparin for non-ST-elevation acute coronary syndrome (NSTE-ACS) patients in a Canadian hospital setting. Methods: An event-based decision analytic model was constructed using clinical and resource use data from OASIS-5, a randomised trial of 20,078 patients from 41 countries. A public payer perspective in the hospital setting was adopted. The probabilities of death, non-fatal myocardial infarction (MI), non-fatal stroke, and major and minor bleeds over a period of 180 days, for both treatment strategies, were estimated using a set of risk equations derived from OASIS-5 data. Resource use data from the trial were valued using Canadian costs. A cost regression model was developed to estimate the mean cost of managing the clinical events over the 180 day period. Annual costs of long-term care for ACS patients were added after 180 days until death. Long-term survival was incorporated using Canadian life tables with further adj...

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)0.003
metaresearch head score (Gemma)0.009
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 score0.099
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.185
GPT teacher head0.379
Teacher spread0.194 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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