Systems dynamics simulation approach to a personalized obesity decision support system model
Bibliographic record
Abstract
Internationally, at the population level, a review of overweight/obesity prevalence trends reveals consistent increase of the condition in industrialized nations, but considerable variability internationally in the magnitude of the change. Unlike other approaches to reduce obesity that rely in expert design of someone's strategy, the decision support model (DSM) proposed by this research goes beyond simply providing a weight loss plan. Arguably, such an approach is one of the major weaknesses of many attempts targeting solutions to the obesity problem. The majority of the efforts fail, if not immediately, months or years after a successful drop in the weight. The proposed DSM employs a multidisciplinary dynamic systems approach. There is little evidence of the use of SD in a healthcare environment for modeling the human body, and, even less in obesity control. The outcome of the DSM accounts for the differences between individuals, so they are able to develop strategies based on their own preferences. The DSM is modular and will allow for the addition of other factors such as non-physical — e.g. emotional, stress, and motivation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".