Estimating Disease Risk of Diabetes Cases in the Presence of Underreporting
Bibliographic record
Abstract
In real life situations, the values of the response variable, which is the count data is mostly under-reported. In this work, we develop a model to cater for under-reporting in the case of count data. In particular, we allow under-reporting to vary spatially by regions through a probability captured by a binomial distribution. Count data mostly comes with a common property, which is the variance is greater than mean. When this happens, the recommended distribution is Negative Binomial (NB) instead of the usual Poisson distribution. The spatial variations of the disease were divided into correlated and uncorrelated parts. When a Negative Binomial was used, both the correlated and uncorrelated parts were all found to share a significant relationship with the relative risk for each region with more of contribution coming from the uncorrelated part. The model obtained was applied to diabetes data in Ghana. Disease maps for the diseases were also developed for Ghana. These maps are critical and informative to policy makers when coming up with preventive mechanisms in the face of scarce resources.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".