Detection and Management of Diabetes during Pregnancy in Low Resource Settings: Insights into Past and Present Clinical Practices
Bibliographic record
Abstract
Background.Timely and adequate treatment is important to limit complications of diabetes affecting pregnancy, but there is a lack of knowledge on how these women are managed in low resource settings.Objective. To identify modalities of gestational diabetes detection and management in low and lower middle income countries.Methods. We conducted a scoping review of published literature and searched the databases PubMed, Web of Science, Embase, and African Index Medicus. We included all articles published until April 24, 2016, containing information on clinical practices of detection and management of gestational diabetes irrespective of publication date or language.Results. We identified 23 articles mainly from Asia and sub-Saharan Africa. The majority of studies were conducted in large tertiary care centers and hospital admission was reported in a third of publications. Ambulatory follow-up was generally done by weekly to fortnightly visits, whereas self-monitoring of blood glucose was not the norm. The cesarean section rate for pregnancies affected by diabetes ranged between 20% and 89%. Referral of newborns to special care units was common.Conclusion. The variety of reported provider practices underlines the importance of promoting latest consensus guidelines on GDM screening and management and the dissemination of information regarding their implementation.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".