Adaptation of the IDF WINGS programme for Hyperglycaemia in Pregnancy in Guyana, South America.
Bibliographic record
Abstract
Objective: Introduced of a protocol for the outpatient management of hyperglycaemia in pregnancy (HIP) in Guyana based on the IDF WINGS programme Design: Quality improvement programme and education intervention Setting: The national referral hospital in Georgetown (GPHC) and two associated community health centres (HC). Population: Pregnant women of <37weeks gestational age. Methods: An inter-professional team of clinical leaders introduced universal screening for gestational diabetes (GDM)using a 75gm OGTT and simplified outpatient management of HIP with self-monitoring of blood glucose, diet followed by metformin then insulin. Main Outcome Measures: Numbers of women screened, diagnosed and treated for HIP. Results: Between November 2016 and 1st July 2019, 2226 pregnant women were screened, 461 25.9%) were abnormal at GPHC and 12 (2.6%) at the HC. Forty-four% were treated with medical nutritional therapy alone, 43% required metformin and 13% received insulin. Caesarian section rates were high (46%) and attendance for postpartum OGTT poor (15%). Conclusions: The high rate of positive tests at GPHC is consistent with the system of transferring high risk patients to GPHC. Before supporting a nationwide universal screening programme, further investigation is required, eg screening for GDM at regional hospitals and HC outside the immediate GPHC catchment. Our results suggest universal screening may not be the only choice for the populations of low-and-middle income countries. Funding: World Diabetes Foundation (WDF) and the Banting and Best Diabetes Centre (BBDC) of the University of Toronto. Keywords: Hyperglycaemia in pregnancy, diabetes, oral glucose tolerance test.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".