Bibliographic record
Abstract
This paper presents a new paradigm for modeling illness in the human population. In this work we propose the development of a patient model using a Mobile Software Agent. We concentrate on Diabetes Mellitus because of the prevalence of this disease and the reality that many citizens must learn to manage their disease through some simple guidelines on their diet, exercise and medication. This form of modeling illness has the potential to predict outcomes for diabetic patients depending on their lifestyle. We further believe that the Patient Agent could be an effective tool in assisting patients to understand their prognosis if they are not meticulous in controlling their blood sugar and insulin levels. The Patient Agent is developed in accordance with the general parameters used in archetypal Diabetes medical tests. Conventional formulae have been applied to transform input variables such as Food, Exercise, and Medications, as well as other risk factors like Age, Ethnicity, and Gender, into output variables such as Blood Glucose and Blood Pressure. The time evolution of the Patient Agent is represented through the outputs which deteriorate over the long term period.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".