Background Factors Determining the Introduction and Dosage of Insulin in Women With Gestational Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes mellitus (GDM) is a risk for perinatal complication, and appropriate diagnosis of and intervention in this condition are important. This study aimed to identify patient factors associated with introduction and dosage of insulin, which is the main drug for treatment of GDM. METHODS: In total, 114 patients who had been diagnosed with GDM at our hospital were included in this study. We retrospectively collected clinical parameters of GDM patients, including how many times positive glucose tolerance test results were obtained, whether insulin was introduced, dosage of insulin, body weight, and infant weight. Background factors differing between the insulin introduction and non-introduction groups of GDM patients and parameters associated with the insulin dosage were analyzed. RESULTS: Insulin was introduced in 51 GDM patients (45%). In the insulin introduction group, the six-divided diet was less common and the 75-g glucose tolerance test result was positive a significantly greater number of times compared with the non-introduction group. The factor associated with the insulin introduction status was the number of positive 75-g glucose tolerance test results (odds ratio (OR) 2.04, 95% confidence interval (CI): 1.09 - 3.81, P value = 0.025). In addition, the insulin dosage was found to positively correlate with body weight in the non-pregnant state (P value = 0.005). CONCLUSIONS: The six-divided diet was effective for blood glucose control in GDM women. A positive correlation found between the insulin dosage and body weight in the non-pregnant state suggests the importance of proper pre-pregnancy body weight control.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".