A walking intervention improves capillary glucose control in women with gestational diabetes mellitus: a pilot study
Bibliographic record
Abstract
Exercise prescriptions that can be translated into clinical recommendations are clearly needed for women with gestational diabetes mellitus (GDM). A pilot project was developed to document the effectiveness of a structured low-intensity walking protocol on capillary glucose control in GDM women. Ten GDM women followed conventional management of diet and insulin therapy, plus a low-intensity walking program (W) from diagnosis to delivery. Capillary glucose concentrations, insulin requirements, and pregnancy outcomes were compared with a matched cohort by body mass index (BMI), age, and insulin usage (20 GDM women who followed conventional management alone (C)). Baseline capillary glucose concentrations were not significantly different between the W and C groups. The W group had an average acute drop in capillary glucose concentration from pre- to post-exercise of 2.0 mmol x L(-1). In addition, the W group had significantly lower mean glucose concentrations in the fasted state and 1 h after meals than the C group in the week prior to delivery. These lower glucose concentrations were achieved while requiring fewer units of insulin per day (C, 0.50 +/- 0.37 U x kg(-1); W, 0.16 +/- 0.13 U x kg(-1); p < 0.05), injected less frequently. These results suggest an effective role in glucose regulation for this structured walking program.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".