Parametric Survival Modeling of Tuberculosis Data- A Case Study of Federal Medical Centre, Bida, Nigeria
Bibliographic record
Abstract
The research performed parametric survival analysis of Tuberculosis (TB) data (covering 2010 to 2016) collected from the Federal Medical Centre, Bida, Niger State, Nigeria. Three parametric survival models (Exponential, Weibull and Log-logistic) were fitted. The outcome variable was time to recovery from TB infection and four covariates being age, gender, TB type and occupation were involved. Models were estimated by maximum likelihood method and model selection criterion used was the Akaike Information Criterion (AIC). The exponential and log-logistic models found all covariates statistically insignificant while Weibull found all covariates but TB type significant at 5% level. Based on AIC, Weibull model with AIC of 163.5731 performed best, followed by log-logistic model with AIC of 191.419 and exponential model performed worst, with AIC of 517.9652. The best of fitted models being Weibull suggested that older patients had higher hazards than younger ones, older patients hence, had lower survival times, holding other covariates constant. That is, the older the TB patient, the lower was the time to recovery from TB. Males had higher hazards and hence, lower survival times compared to females. That is, male TB patients recovered faster than the females. Pulmonary TB patients had lower (insignificant) hazards and hence, higher survival times than Respiratory TB patients. TB patients on technical occupation had lower hazards than others and hence, had higher survival times than those whose occupations were considered not technical. The research concluded that age, gender and occupation were the major determinants of recovery period of TB patients. It was recommended that the Management of Federal Medical Centre, Bida, and other organizations involved in TB management could make use of the Weibull model to fit and predict both the survival and hazard rates of TB patients.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".