Estimating life expectancy among older multimorbid adults to personalize preventive care
Bibliographic record
Abstract
Abstract Background Providing high value care and avoiding care overuse is a challenge among older multimorbid adults. There is evidence on benefits and harms of cancer screening and cardiovascular diseases (CVD) preventive treatment up to the age of 75. However, this evidence is not directly applicable to older multimorbid patients. Because each cancer and CVD preventive care has a specific lagtime to benefit, many guidelines recommend tailoring preventive care according to the estimated life expectancy (LE). However, there is no tool to estimate LE among multimorbid patients. Our objectives are therefore to develop new mortality risk prognostic indices and to derive a new LE estimator, what will help clinicians tailoring preventive care in older multimorbid adults. Methods and Results We conduct a prospective cohort study by extending the follow-up of 822 patients in Bern, Switzerland, included in the OPtimising thERapy to prevent Avoidable hospital admissions in Mulitmorbid older people (OPERAM) study over 3 years. Detailed information about cancer screening and CVD preventive treatment will be collected. We will identify variables independently associated with mortality and weight the variables to create 1 year and 3 year mortality prognostic indices. We will transform the 3 year prognostic index into a LE estimator. Preliminary results will be presented at the congress. Conclusions We will develop the first life expectancy estimator specifically for older multimorbid adults. This tool will help clinicians to tailor cardiovascular and cancer preventive care in older multimorbid adults. Key messages Because of the lagtime to benefit, personalizing preventive care by estimated life expectancy is recommended. We will provide the first life expectancy estimator for older multimorbid adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".