Life expectancy in multimorbid older adults: Why it matters for preventive care
Bibliographic record
Abstract
Abstract Multimorbidity is highly prevalent among older adults and associated with a shorter life expectancy. Many guidelines recommend tailoring preventive care of multimorbid people according to life expectancy. Indeed, there is a time lag between a preventive care intervention and the expected potential benefit, and patients with a relatively short life expectancy might not have the time to benefit from the preventive care intervention. Further, both patients and health care providers tend to overestimate benefits and underestimate risks of interventions. It is therefore necessary to have a valid index for mortality prediction in multimorbid patients, but there is no life expectancy estimator designed and recommended for this population. The paper describes the development and internal validation of a new life expectancy estimator. In this presentation, we focus on the importance of life expectancy estimation in multimorbid older adults: Why does it matter in this population? What is the time lag to benefit of a preventive intervention, e.g., cancer screening? What is the state in this field, in research and clinical practice? How could tailoring preventive care to life expectancy improve patient outcomes?
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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 | 0.020 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".