Bibliographic record
Abstract
We thank our Canadian colleagues for their interest in our recent article and, in particular, their observations on the need for the 2-h sample with a 75-g OGTT. We note that 25% of their large cohort of 10 773 women had an abnormal 2-h sample using the IADPSG diagnostic criteria and therefore their understandable caution about missing the diagnosis of gestational diabetes mellitus. However, their report differs from ours in several respects. Their population had a previous abnormal 50-g glucose challenge test whereas our population had not been previously screened. Our population was selectively screened and we assume their population was universally screened. Also, they used a Serum Separator tube and measured serum glucose after leaving samples to clot for 30 min, whereas we measured plasma glucose after strictly applying the ADA laboratory standards, including the use of a fluoride-EDTA tube placed immediately on an ice-slurry with centrifugation and analysis within 30 min. We are not surprised therefore that there are differences in the incidence of abnormal 2-h measurements. We are also uncertain how many women in their report had an abnormal fasting and 1-h sample as well as an abnormal 2-h sample? We believe that is the key question because if one of the earlier samples was abnormal, the 2-h becomes unnecessary for diagnosis. Our article did state that our observations would need to be repeated in larger studies and we welcome this dialogue because it highlights the importance of the need to standardize both the preanalytical and the analytical laboratory standards in evaluating the contribution of abnormal fasting, 1-h and 2-h samples in diagnosing gestational diabetes mellitus.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.034 | 0.039 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".