How to incorporate chronic health conditions in oncologic decision-making and care for older patients with cancer? A survey among healthcare professionals
Bibliographic record
Abstract
PURPOSE: A substantial proportion of patients with cancer are older and experience multimorbidity. As the population is ageing, the management of older patients with multimorbidity including cancer will represent a significant challenge to current clinical practice. METHODS: This study aimed to (1) identify which chronic health conditions may cause change in oncologic decision-making and care in older patients and (2) provide guidance on how to incorporate these in decision-making and care provision of older patients with cancer. Based on a scoping literature review, an initial list of prevalent morbidities was developed. A subsequent survey among healthcare providers involved in the care for older patients with cancer assessed which chronic health conditions were relevant and why. RESULTS: A list of 53 chronic health conditions was developed, of which 34 were considered likely or very likely to influence decision-making or care according to the 39 healthcare professionals who responded. These conditions were further categorized into five patient profiles. From these conditions, five patient profiles were developed, namely, (1) a somatic profile consisting of cardiovascular, metabolic, and pulmonary disease, (2) a functional profile, including conditions that cause disability, dependency or a high caregiver burden, (3) a psychosocial profile, including cognitive impairment, (4) a nutritional profile also including digestive system diseases, and finally, (5) a concurrent cancer profile. All profiles were considered likely to impact decision-making with differences between treatment modalities. The impact on the care trajectory was generally considered less significant, except for patients with care dependency and psychosocial health problems. CONCLUSIONS: Chronic health conditions have various ways of influencing oncologic decision-making and the care trajectory in older adults with cancer. Understanding why specific chronic health conditions may impact the oncologic care trajectory can aid clinicians in the management of older patients with multimorbidity, including cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".