The calculation of critical illness insurance premiums with terminal illness condition
Bibliographic record
Abstract
Long Term Care (LTC) insurance is a treatment insurance related to critical illness conditions.An insured with LTC insurance does not have to worry about the medical care he will need when diagnosed with an illness or undergoing medical treatment.Terminal illness is a critical illness condition when it has reached the final stage and is diagnosed by a specialist that the life expectancy of the insured is less than 12 months since the diagnosis was given.The types of critical illnesses used in this study are cancer, heart disease, stroke, and diabetes mellitus which are the dominant causes of death in Indonesia.The data that used in this research is the form of Indonesia's 2019 mortality table and the data of critical illness patients with terminal illness conditions prevalence from Riskesdas 2018.The single net premium value that need to be paid in this study was obtained by determining the transition probabilities of the multi-state model with 10 states from prevalence of disease and the prevalence of death.Based on the case study, as an example, the amount of single net premium that must be paid by an insured male aged 35 years in good health is IDR 12,692,741 for the protection period and the payment period is 10 years.The compensation value obtained when the insured diagnosed with a terminal illness or die is IDR 300,000,000 where the interest rate used is 3.75%.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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 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".