Bibliographic record
Abstract
Age is an independent risk factor for cardiovascular disease. With the accelerated growth of the population of older adults, geriatric and cardiac care are becoming increasingly entwined. Although cardiovascular disease in younger adults often occurs as an isolated problem, it is more likely to occur in combination with clinical challenges related to age in older patients. Management of cardiovascular disease is transmuted by the context of multimorbidity, frailty, polypharmacy, cognitive dysfunction, functional decline, and other complexities of age. This means that additional insight and skills are needed to manage a broader range of relevant problems in older patients with cardiovascular disease. This review covers geriatric conditions that are relevant when treating older adults with cardiovascular disease, particularly management considerations. Traditional practice guidelines are generally well suited for robust older adults, but many others benefit from a relatively more personalized therapeutic approach that allows for a range of medical circumstances and idiosyncratic goals of care. This requires weighing of risks and benefits amidst the patient's aggregate clinical status and the ability to communicate effectively about this with patients and, where appropriate, their care givers in a process of shared decision making. Such a personalized approach can be particularly gratifying, as it provides opportunities to optimize an older patient's function and quality of life at a time in life when these often become foremost therapeutic priorities.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".