Transition Intensities for Critical Illness: A Study on Canadian Health Data
Bibliographic record
Abstract
Multiple state model is a mathematical model which is characterised by two important elements, transition intensity and transition probability. Critical illness has increased rapidly which is alarmed by the healthcare experts, and becoming an important concern in society. In this paper, by using the Canadian health data, we provide an estimation of transition intensities from the healthy state to the critical illness state with the application of prevalence rate. We provide a discrete calculation of transition intensities with some mathematical formula discussed by some previous studies. Next, we assume that the transition intensities of critical illnesses and death due to other causes are modelled by Gompertz and Makeham mortality models. We also compare and estimate the transition intensities of critical illnesses and dead due to other causes between these two models using a model selection method. We observe the sensitivity of the Gompertz and Makeham models with the different values of extra mortality . Lastly, we obtain and present the numerical results of the transition intensities of healthy lives to critical illness with the Canadian health data.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".