Standard Period Life Table Used to Compute the Life Expectancy of Diseased Subpopulations: More Confusing Than Helpful
Bibliographic record
Abstract
Life expectancy (LE) based on a period life table (PLT) traditionally serves as a general population summary metric. It is, however, becoming more frequently reported for chronically afflicted subpopulations. In general populations, there is always an obvious real cohort sharing the hypothetical PLT cohort characteristics, and the LE estimate is intuitively understood as that real cohort mean survival time, assuming constancy of death risks. In diseased subpopulations, the correspondence between the hypothetical cohort and a real cohort is not straightforward. Furthermore, the excess mortality of chronic diseases usually changes according to age at onset and time since onset. The standard PLT method does not allow for proper control of these issues, so the LE estimate can only be deemed valid under specific assumptions. Without clear statements about the real cohort to whom the estimate is intended and the assumptions allowing disregard of the effect of age at onset and time since onset, LEs of afflicted subpopulations computed with the PLT are only abstract numbers summarizing mortality rates. If called "life expectancy," they can be seriously misleading. The same applies to health-adjusted LE.
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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 | 0.008 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".