Response to Comment on Barbour and Feig. Metformin for Gestational Diabetes Mellitus: Progeny, Perspective, and a Personalized Approach. Diabetes Care 2019;42:396–399
Bibliographic record
Abstract
We appreciate Dr. Rowan’s thoughtful comments (1) on our article (2), as her comments make a number of important points. As she notes, there are conflicting mouse data with respect to the effect of metformin. Furthermore, it is difficult to extrapolate mouse studies to humans given that there are marked differences in placentation and oxidative/proliferative pathways, the early postnatal period may have stronger developmental effects than later pregnancy, and mice are typically born with only 2–3% fat compared with 6–14% fat in humans. However, in one of the mouse studies Dr. Rowan cited, prenatal metformin did lead to increased weight gain, mesenteric fat, and liver weight after a high-fat diet, and males showed glucose intolerance (3). Dr. …
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.024 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".