Supportive care and healthcare service utilisation in older adults with a new cancer diagnosis: a population-based review
Bibliographic record
Abstract
OBJECTIVES: Older adults have unique needs and may benefit from additional supportive services through their cancer journey. It can be challenging for older adults to navigate the siloed systems within cancer centres and the community. We aimed to document the use of supportive care services in older adults with a new cancer diagnosis in a public healthcare system. METHODS: We used population-based databases in British Columbia to document referrals to supportive care services. Patients aged 70 years and above with a new diagnosis of solid tumour in the year 2015 were included. Supportive care services captured were social work, psychiatry, palliative care, nutrition and home care. Chart review was used to assess visits to the emergency room and extra calls to the cancer centre help line. RESULTS: 2014 patients were included with a median age of 77, 30% had advanced cancer. 459 (22.8%) of patients accessed one or more services through the cancer centre. The most common service used was patient and family counselling (13%). 309 (15.3%) of patients used community home care services. Patients aged 80 years and above were less likely to access supportive care resources (OR 0.57) compared with those 70-79 years. Patients with advanced cancer, those treated at smaller cancer centres, and patients with colorectal, gynaecological and lung cancer were more likely to have received a supportive care referral. CONCLUSIONS: Older adults, particularly those above 80 years, have low rates of supportive care service utilisation. Barriers to access must be explored, in addition to novel ways of holistic care delivery.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".