Determining starting basal rates of insulin infusion for insulin pump users: a comparison between methods
Bibliographic record
Abstract
OBJECTIVE: We aimed to assess the accuracy and safety of presently available methods of estimating starting basal insulin rates for patients with type 1 and 2 diabetes, and to compare them against an empirically derived standard basal rate and a newly developed regression formula. RESEARCH DESIGN AND METHODS: Data on 61 patients with type 1 diabetes on continuous subcutaneous insulin infusion (CSII) therapy and 34 patients with type 2 diabetes on CSII were reviewed. Patient data were first analyzed for correlations between initial patient parameters and final basal rates. Starting basal rates were then retrospectively calculated for these patients according to the weight-based method (WB-M), the total daily dose (TDD) of insulin method (TDD-M), a flat empiric value, and a new formula developed by regression analysis of clinical data. These 4 methods were subsequently compared in their accuracy and potential risk of hypoglycemia. RESULTS: For type 1 diabetes, patient weight and TDD of long-acting insulin correlated with final basal rates. Both the regression formula and the TDD-M appeared safer than the WB-M and empirical estimates. For type 2 diabetes, only patient TDD of long-acting insulin correlated with final basal rates. The regression formula was significantly more accurate for patients with type 2 diabetes overall, but the TDD-M estimate was marginally safer. CONCLUSIONS: The pre-existing TDD-M was found to be the safest presently recommended estimate of initial basal rates for pump initiation in both type 1 and 2 diabetes. The best-fit regression was found to have potential use for type 2 CSII initiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".