PROTOCOL Outcomes in the offspring n K
Bibliographic record
Abstract
estimated that about 470 million people will be diag-nosed with pre-diabetes globally by the year 2030 [1]. During pregnancy, women can develop a dysglycemic state known as gestational diabetes mellitus (GDM). Swaleh et al. Systematic Reviews (2015) 4:67 DOI 10.1186/s13643-015-0051-1been found to influence fetal development and has bothHamilton, Ontario L8N 3Z5, Canada Full list of author information is available at the end of the articlePre-diabetes includes impaired fasting glucose, impaired glucose tolerance, and hemoglobin A1c of 6 % to 6.4% [2]. Currently, the crude prevalence of pre-diabetes in the United States is roughly 30 % among individuals greater than 20 years old [3]. The Canadian Diabetes Association (CDA) estimates that 5.7 million out of the Although most women with GDM have normal blood sugars after delivery, the risk of developing subsequent T2DM is higher [7]. The diagnostic criteria for GDM vary internationally but generally involve a two-step process: an initial screening challenge with glucose followed by a standardized 75 (or 100) g oral glucose tolerance test (OGTT) if the screen is abnormal. More details on the classification of GDM are included in the methods. This dysglycemic state during pregnancy has * Correspondence:
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.217 | 0.049 |
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".