Survival Modelling of Tuberculosis Data-A Case Study of Federal Medical Centre, Bida, Nigeria
Bibliographic record
Abstract
The article aimed at fitting Cox-proportional hazards model to Tuberculosis (TB) data. TB data on 259 patients spanning 2010 through 2016 were collected from the Federal Medical Centre, Bida, Nigeria. Covariates involved were gender, age, type of TB and occupation. Fifteen different Cox models, representing all possible combinations of covariates in question were fitted. Parameters were estimated by method of maximum partial likelihood and model selection was based on Akaike information criterion (AIC). Model (G+C), with gender and occupation as covariates produced the least AIC of 618.597 and hence, was adjudged the best. That is, gender and occupation constituted the best subset of covariates that explained survival of TB patients. The model suggested that recovery hazard of a male TB patient was 24.1% lower than that of a female patient possessing same occupation. This implies that male patient had higher survival time than the female having same occupational status. It further suggested that recovery hazard for patient on technical occupation was 27.46% higher than for patient on non-technical job and of same gender. Hence, a patient on technical occupation had reduced survival time compared to one of same gender on non-technical occupation. It was concluded that gender and occupation explained best, survival of TB patients based on AIC.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| 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".