Bibliographic record
Abstract
Clinical decision analyses use time horizons that vary from hours to the patient's entire life. Analyses of decisions with a lifetime horizon commonly use Markov models, which simulate the patient's lifespan by dividing it into equal periods (cycles). At each cycle, the model exposes a hypothetical cohort to the competing hazards of normal aging and of the disease in question (disease-specific hazards), and the results are presented as years of life expectancy. This paper highlights two limitations of lifetime Markov models that have been ignored in recent publications. First, since there are no readily available data on changes in disease-specific hazards over time, these hazards are often derived from short-term follow-up studies, and assumed to be constant over the patient's entire life. Second, results may be better presented in terms of health states (i.e. proportions of patients expected to recover completely, recover with a disability or die) rather than life expectancy. Although well-known, these two limitations require re-emphasis. They may be avoided by restricting the time horizon of decision analyses and presenting results as health states as well as life expectancies. When a lifetime horizon is necessary, the performance of Markov models may be improved by the using of time-variant disease-specific hazards derived from long-term follow-up studies, or from theoretical models that simulate more closely the disease progression over time, rather than assuming constant disease-specific hazards.
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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 | 0.034 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".