Analysis of Reported Cases of Diabetes Disease in Nigeria: A Survival Analysis Approach
Bibliographic record
Abstract
The goal of this study is to look at the survival time distribution for diabetes patients at the National Hospital Abuja, taking into account a variety of variables. The Kaplan Meier-estimator indicated that there is no statistically significant difference in the distribution of survival time by sex, despite the fact that married patients were seen to live longer than single patients. Patients in urban and rural areas had the same estimated survival distribution after testing. It is observed that the Cox proportional model was significant when tested since the p-value = 0.000 was less than the 0.05 threshold. The distribution of survival time for patients with diabetes is shown to be substantially different for patients of the four age categories included in the study, indicating that the relative risk of patients is based on age. Every patient is predicted to acquire the danger at about the same time, with no sex-related multiplication impact. It was found out that the disease's prevalence is unaffected by several of the variables studied, indicating that more regular medical checks are required.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".