Bibliographic record
Abstract
Glucose monitoring is essential for the successful management of gestational diabetes. The accuracy of glucose meters is typically determined over a much wider range of glucose values than that commonly encountered in gestational diabetes. The objective of our study was to look at the accuracy of self-monitoring glucose meters in a clinic setting over a range of glucose values seen in gestational diabetes. We retrospectively analyzed 107 case records of subjects with gestational diabetes, each of whom had three simultaneous laboratory and glucose meter glucose tests. The results were compared using the performance goals that (1) all of glucose meters should have readings within 10% of the reference value and (2) the error grid analysis in the standard format and a modified version suitable for gestational diabetes. We also examined the range of the differences from the reference value. Nearly half of the values (47%) were in excess of 10% of the reference range (either above or below). Close to 15% were in excess of 20% difference from the reference range. Standard error grid analysis showed that 96% of the values fell within sections A of the error grid which are considered acceptable, and 100% fell within sections A and B, differences which are generally considered to have no major impact on care. The modified version of the error grid analysis demonstrated that 39% of the values were outside the acceptable range. Within subjects, a substantial number (26%) had a range of differences that exceeded 20% difference between each other. Although the meters give reasonable results that might be acceptable for general diabetes care, the results provide some cause for concern in the management of gestational diabetes. Given the need for precision in the setting of pregnancy particularly in making the decision of whether to start or withhold insulin therapy, caregivers need to be cognizant of these inaccuracies.
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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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".