351-OR: Continuous Glucose Monitoring in Pregnant Women with Type 1 Diabetes: Cost-Effectiveness Analyses of the CONCEPTT Randomized Controlled Trial
Bibliographic record
Abstract
Aims: To evaluate the cost-effectiveness of continuous glucose monitoring (CGM) in type 1 diabetes (T1D) pregnancy. Methods: Decision analytical models to compare T1D antenatal care with and without use of CGM. Probabilities, maternal health state utilities and healthcare resource utilization were obtained from individual patient-trial data. Costs and neonatal health state utilities were obtained from the literature. Primary outcome of interest was neonatal quality adjusted life years (QALY). The willingness to pay threshold was £30,000/QALY. Results: Direct costs of CGM use were £2,045. From a neonatal perspective, CGM during was cost saving (- £2,613), and effective (75.43 vs. 73.77 QALYs), with an incremental cost effectiveness ratio (ICER) - 1,570.57/QALY (Figure 1). Sensitivity analyses demonstrated robustness of the model across ranges of variables including varying NICU care for preterm and term neonates, and health state utilities. From a maternal perspective, CGM was associated with additional cost (£330), but remained effective; QALYs 61.33 vs. 61.27 and ICER £5,508.00/QALY. CGM remained efficacious and the favored treatment strategy in the sensitivity analyses performed. Conclusions: CGM use during T1D pregnancy is cost saving from a neonatal perspective and associated with improved cost effectiveness for both mother and neonate. Disclosure H.R. Murphy: Advisory Panel; Self; Medtronic MiniMed, Inc. D. Feig: Advisory Panel; Self; Medtronic. Speaker's Bureau; Self; Medtronic. N. Patel: None. Funding JDRF (17-2011-533); JDRF Canadian Clinical Trial Network (80-2010-585)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".