Insulin Therapy to Improve BMI in Cystic Fibrosis–Related Diabetes Without Fasting Hyperglycemia
Bibliographic record
Abstract
OBJECTIVE: Cystic fibrosis-related diabetes (CFRD) without fasting hyperglycemia (CFRD FH-) is not associated with microvascular or macrovascular complications, leading to controversy about the need for treatment. The Cystic Fibrosis Related Diabetes Therapy (CFRDT) Trial sought to determine whether diabetes therapy improves BMI in these patients. RESEARCH DESIGN AND METHODS: A three-arm multicenter randomized trial compared 1 year of therapy with premeal insulin aspart, repaglinide, or oral placebo in subjects with cystic fibrosis who had abnormal glucose tolerance. RESULTS: One hundred adult patients were enrolled. Eighty-one completed the study, including 61 with CFRD FH- and 20 with severly impaired glucose tolerance (IGT). During the year before therapy, BMI declined in all groups. Among the group with CFRD FH-, insulin-treated patients lost 0.30 +/- 0.21 BMI units the year before therapy. After 1 year of insulin therapy, this pattern reversed, and they gained 0.39 +/- 21 BMI units (P = 0.02). No significant change in the rate of BMI decline was seen in placebo-treated patients (P = 0.45). Repaglinide-treated patients had an initial significant BMI gain (0.53 +/- 0.19 BMI units, P = 0.01), but this effect was not sustained. After 6 months of therapy they lost weight so that by 12 months there was no difference in the rate of BMI change during the study year compared with the year before (P = 0.33). Among patients with IGT, neither insulin nor repaglinide affected the rate of BMI decline. No significant differences were seen in the rate of lung function decline or the number of hospitalizations in any group. CONCLUSIONS: Insulin therapy safely reversed chronic weight loss in patients with CFRD FH-.
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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 | 0.001 |
| 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) | 0.002 | 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".