Estimating risk and cost: An analysis of patients with risk factors for Type 2 Diabetes in rural Jalisco, Mexico
Bibliographic record
Abstract
The global age standardized prevalence of type 2 diabetes (T2DM) has doubled (4.7% to 8.5%) over the last three decades and is increasing more rapidly in low and middle-income countries (LMICs). The global economic burden of diabetes affects individuals and health care systems and is estimated to cost $825 billion USD a year. Within Mexico, T2DM is the second leading cause of mortality and the leading cause of morbidity using disability associated life years (DALYs). A retrospective chart review and cost analysis, analyzing those at risk of diabetes, was conducted at a rural community health clinic in Jalisco, Mexico. The goal was to project the cost of providing an appropriate scope of care and plan prevention-based population health programs. The results demonstrated that out of 264 charts reviewed, 218 (83%) had one or more diabetic risk factor. The estimated per patient per visit cost is $127.22 MP (Mexican Peso, 2018) and as the number of diabetes risk factors increases for an individual patient, the mean cost of their care to the system increases (p < .001). Those with at least one risk factor comprise the majority in both males and females with a median age of 36 and median BMI of 28, and this group also has the highest percentage of borderline hypertension (46%). This data demonstrates an opportunity to intervene in a group of young adults (ages 27-46) with a cluster of high-risk borderline risk factors and preventing them from developing obesity, hypertension and diabetes later in life.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".