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Record W4399687988 · doi:10.2337/db24-1051-p

1051-P: Cost-Effectiveness of Flash CGM Compared with SMBG—A Canadian Private-Payer Perspective

2024· article· en· W4399687988 on OpenAlexaboutno aff
Stewart B. Harris, Sal Cimino, THI THANH YEN NGUYEN, Yeesha Poon, Kirk Szafranski

Bibliographic record

VenueDiabetes · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationCost effectivenessFamily medicineActuarial scienceBusinessEnvironmental healthRisk analysis (engineering)

Abstract

fetched live from OpenAlex

Introduction & Objective: For people living with diabetes, effective glucose monitoring is recognized as a key component in diabetes care to reduce disease burden, complications, and healthcare utilization. This study aimed to assess the cost-effectiveness of flash CGM, compared with SMBG, from the perspective of a Canadian private payer. Methods: A recently developed patient-level microsimulation model was used to compare flash CGM with SMBG. Time horizons of 40 years (T1DM) and 25 years (T2DM) were used, reflecting the period of typical private payer coverage. Costs and utilities were discounted at 1.5%. Population characteristics, treatment outcomes, and utilities were based on RCTs and RWE studies. Mean age at model entry was 23 years for T1DM and 40 years for T2DM. T2DM treatment assumptions were: 84% non-insulin; 10% basal insulin; and 6% MDI. Costs were taken from Canadian sources, and included flash CGM and SMBG acquisition costs, the costs to private payers of treating diabetes complications, and absenteeism costs. The primary outcome was cost per quality-adjusted life year (QALY). Results: For both T1DM and T2DM, flash CGM provided more QALYs than SMBG while reducing costs (Table). Scenario analyses were consistent with the base-case results. Conclusions: From a Canadian private payer perspective, flash CGM is cost effective compared with SMBG for all people living with diabetes. Disclosure S.B. Harris: Consultant; Abbott. Research Support; Boehringer-Ingelheim. Consultant; Dexcom, Inc. Advisory Panel; Eli Lilly and Company. Consultant; Eli Lilly and Company, Novo Nordisk, Sanofi. Research Support; Novartis AG. Consultant; Bayer Inc. S. Cimino: Consultant; Abbott, Boehringer-Ingelheim, Eisai Inc., Eli Lilly and Company, Merck & Co., Inc., Novartis Canada, Novo Nordisk Canada Inc., Takeda Canada. T. Nguyen: None. Y. Poon: Employee; Abbott. K. Szafranski: Consultant; Abbott, Novo Nordisk, Novartis Canada, CSL Behring, AbbVie Inc., Sanofi.

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 score0.149
Threshold uncertainty score0.299

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 communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.204
GPT teacher head0.393
Teacher spread0.189 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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