Prediction of acute and chronic complications by a new computer simulation model for type 1 and type 2 diabetes: the Diabetes Mel l it us Model (DMM)
Bibliographic record
Abstract
SummaryAn epidemiological simulation model for patients with type 1 and type 2 diabetes (the Diabetes Mellitus Model (DMM)) was developed based on published clinical and observational data and expert estimations, for prediction of short- and long-term outcomes in defined patient cohorts. A computer program was developed with an interface for definition of patient cohorts and for results display. Patient cohorts can be user-defined by gender, age, duration and type of diabetes, glycosylated haemoglobin, blood pressure, albumin excretion and therapy. Based on riskequations and current risk variable levels, the DMM simulates complications over 10 years (hypoglycaemia; retinopathy; blindness; microalbuminuria and macroalbuminuria; end-stage renal disease; neuropathy; amputation; diabetic foot syndrome; myocardial infarction; stroke; angina pectoris; heart failure; and death). The DMM is suitable for simulation of complications and for estimation of clinical implications of various diabetes care strategies, and may be particularly valuable in lieu of long-term clinical trial data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".