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Record W4393025272 · doi:10.1007/s41999-023-00919-2

How to incorporate chronic health conditions in oncologic decision-making and care for older patients with cancer? A survey among healthcare professionals

2024· article· en· W4393025272 on OpenAlexaff
Nelleke Seghers, Siri Rostoft, Shane O’Hanlon, Anita O’Donovan, Karlijn J.G. Schulkes, Isacco Montroni, Johanneke E.A. Portielje, Hans Wildiers, Pierre Soubeyran, Marije E. Hamaker

Bibliographic record

VenueEuropean Geriatric Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsTrinity College
FundersH2020 Societal Challenges
KeywordsMedicinePsychosocialHealth careModalitiesDiseasePopulation ageingChronic conditionPopulationCancerChronic careGerontologyFamily medicineChronic diseasePsychiatryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.354
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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