Bibliographic record
Abstract
Technology is increasingly proving beneficial in helping patients with diabetes achieve better glycemic control with less hypoglycemia. However, there are little data during pregnancy. Old randomized trials using insulin pump during pregnancy have not shown improvements in glycemic control, while more recent cohort studies obtained variable results with either similar or worse glycemic control and neonatal outcomes. Considering these still unsatisfactory results, many expectations have been raised by the introduction of continuous glucose monitoring (CGM). “Professional” CGM has proved valuable as an investigational tool, giving deeper insight into glucose pathophysiology and effects of diabetes in perinatal outcomes, but its routine clinical application was predominantly disappointing on pregnancy outcome. More recently, real-time CGM (RTCGM) seems to offer the most interesting prospects. While an earlier trial using intermittent RTCGM was not very encouraging, the CONCEPTT study, a multicenter, randomized trial of continuous use of CGM showed improved glycemic control and neonatal outcomes. Preliminary data from closed-loop studies in pregnancy show improved nocturnal time in target and less hypoglycemia. Daytime time in target with postprandial highs remain a challenge. Further large randomized trials in pregnancy with hybrid closed-loop systems are needed to show safety and efficacy in the broader pregnant population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".