The effectiveness of implementing a reminder system into routine clinical practice: does it increase postpartum screening in women with gestational diabetes?
Bibliographic record
Abstract
INTRODUCTION: During regular care, women with previous gestational diabetes mellitus (GDM) rarely receive the recommended screening test for type 2 diabetes, a 2-hour oral glucose tolerance test (OGTT), in the postpartum period. The current study examined whether the implementation of a reminder system improved screening rates. METHODS: Based on our previous randomized control trial, we implemented a postpartum reminder (letter or phone call) protocol into routine care at two of three clinical sites. We verified postpartum testing by searching hospital laboratory databases and by linking to the provincial physician service claims database. The primary outcome was the proportion of patients who underwent an OGTT within 6 months of delivery. RESULTS: Women who received care in a setting using a reminder system were more likely to receive an OGTT within 6 months postpartum (28%) compared with usual care (14%). The OGTT rates for both reminder groups were lower than that found in our randomized control trial (28% vs. 60%). CONCLUSION: Although the screening rates remain low, postpartum reminders doubled screening rates using the recommended test, the 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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".