Weibull Regression Model on Hospitalization Time Data of COVID-19 Patients at Abdul Wahab Sjahranie Hospital Samarinda
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Bibliographic record
Abstract
The Weibull regression model is a Weibull distribution that is directly influenced by covariates. The Weibull regression model discussed in this study was the Weibull survival and the Weibull hazard regression model. The Weibull regression model in this study was applied to the hospitalization time data of COVID-19 patients from May to September 2021 at the RSUD Abdul Wahab Sjahranie Samarinda. The event of the study is recovery of patient. This study aims to obtain Weibull survival and hazard regression model to the hospitalization time data of Covid-19 patients, to obtain the factors that affect the chance of not recovering (survive) and the recovery rate of Covid-19 patients, and also to interpret Weibull survival and hazard regression models based on the obtained model. In this study, the Maximum Likelihood Estimation (MLE) was used as the parameter estimation method. The closed form of the Maximum Likelihood (ML) estimator cannot be found analytically, and the approximation of ML estimator was found using Newton-Raphson iterative method. Based on the test results, the factors that influence the chance of not recovering and the recovery rate of COVID-19 patients were comorbidities history. The chance of not not recovering (survive) for patients who have a history of comorbidities is greater than the chance of not recovering (survive) for patients who have no history of comorbidities. The recovery rate for COVID-19 patients who have a history of comorbidities is 0,5358 times the recovery rate for patients without a history of comorbidities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it