Gestational diabetes mellitus screening and diagnosis: a prospective randomised controlled trial comparing costs of one‐step and two‐step methods
Bibliographic record
Abstract
OBJECTIVE: To conduct a cost minimisation analysis of three methods of gestational diabetes mellitus (GDM) screening and diagnosis. DESIGN: Prospective randomised controlled trial. SETTING: University teaching hospital. POPULATION: Pregnant women (n = 1594) presenting for GDM screening. METHODS: Women presenting for GDM screening, who consented to participate, were randomised to GR1 [1-hour, 50-g glucose screen (GS) +/- 3-hour, 100-g oral glucose tolerance test (OGTT)], GR2 (50-g GS +/- 2-hour, 75-g OGTT) or GR3 (2-hour, 75-g OGTT). Demographics, health and time/travel cost information were assessed for each glucose testing visit. MAIN OUTCOME MEASURES: Costs (direct and indirect) and prevalence of GDM diagnosis. RESULTS: The direct sampling costs of the glucose tests per woman were as follows: GS, CAN$12.57; 75-g OGTT, $36.10; 100-g OGTT, CAN$48.13. Among women in the two-step method groups diagnosed with GDM, 39% of the GR1 and 61% of the GR2 groups were diagnosed at the first step by GS > or = 10.3 mmol/l, according to the Canadian Diabetes Association recommendations, contributing to a lower total cost in these groups. The total costs per woman screened were as follows: GR1, CAN$91.61; GR2, CAN$89.03; GR3, CAN$108.38. The GDM prevalence was similar (3.7%, 3.7% and 3.6%, respectively). The higher costs of GR3 were related to more blood draws and the time required for all women to undergo the 2-hour OGTT. CONCLUSIONS: Careful consideration should be given to an internationally recommended method of universal screening for GDM which minimises the burden and cost for individual women and the healthcare system, yet provides diagnostic efficacy. The two-step method (GS +/- OGTT) accomplished this better than the one-step method (75-g OGTT).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".