Bibliographic record
Abstract
functional data analysis, Law et al. demonstrated that a higher mean glucose level is driven by suboptimal nocturnal glucose control and that small differences in nocturnal CGM measures are associated with offspring LGA (7).Another team used spectral clustering approaches to develop a new glucose variability metric, the ''gluocotype'' (8).Hall et al. describe three glucotypes of increasing variability (low, moderate, and severe), which together with mean CGM glucose explain > 70% of temporal glucose variability.This methodology could detect earlier preclinical forms of dysglycemia and identify those most at risk for type 2 diabetes or prediabetes, which is very relevant for women with GDM.Interestingly it also highlighted the imitations of the oral glucose tolerance test (OGTT), which was seemingly normal in one-quarter of participants categorized as having severe glucotypes.Retnakaran et al. describe the impact of higher environmental temperatures on OGTT results, although unfortunately lacked CGM data, to establish whether or not environmental temperatures impact on CGM measures (9).The follow-up study of over 4,700 mother-child pairs from the Hyperglycemic and Adverse Pregnancy Outcomes (HAPO) study confirmed that, women with GDM were more likely to develop type 2 diabetes or prediabetes (52.2 vs 20.1%) than those in the general maternity population (10).Another study not directly involving pregnant women with diabetes but very relevant to maternity and pediatric populations describes the long-term impact of being born LGA (11).In a large German cohort of over 50,000 participants, almost half of all LGA newborns continued to be overweight or obese into adolescence.This highlights the importance of optimizing maternal glucose control in the second and third trimesters to reduce neonatal LGA and the longer-term consequences of overweight and obesity persisting into adolescence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".