The ESCAPADE Study: Early supportive care for advanced cancer patients, assessing care delivery and provider engagement.
Bibliographic record
Abstract
e23142 Background: Advanced cancer patients benefit from early integration of palliative care (EIPC) with usual care. A proposed model of EIPC reserves specialized palliative care (SPC) for complex patients, while primary care providers (PCP) and oncologists oversee basic palliative care (PC). We studied the attitudes among patients and their healthcare providers regarding delivery of EIPC. Methods: A cross-sectional study at a tertiary cancer centre in Ontario. Patients with newly diagnosed incurable gastrointestinal (GI) cancer were surveyed using a study specific instrument for the outcomes of interest: importance of and preferences for accessing support across 8 domains of PC (disease management, physical, psychological, social, spiritual, practical, end of life care, loss and grief). Healthcare providers within the circle of care completed a parallel survey for each recruited patient. Primary analysis involved use of descriptive statistics to summarize survey results and concordance between patient and provider responses. Results: From Oct 2017 - Nov 2018, 67 patients were surveyed (median age 69, 34% female). 90% had an identified medical oncologist, and 19% had SPC. 97% had a PCP, but only 42% listed a PCP as part of the care team. Median time from first oncology assessment for advanced cancer to patient survey completion was 52.5 days. 85 providers responded (oncologist = 59, PCP = 20, SPC = 6; response rate 92%; 1-3 physician responses per patient). Disease management and physical concerns were most important to patients. In these domains, 67% and 81% of patients endorsed receiving care from the preferred provider, but concordance between patient and physician responses regarding most responsible provider was only 58% and 38%. For all other domains, 87 – 100% of patients attributed primary responsibility to self or family rather than any healthcare provider. Conclusions: Respondents did not assign responsibility to physicians early in the disease trajectory for many domains of PC. Our findings suggest that incorporating patient activation and empowerment into EIPC requires further study. PCPs appeared to have limited involvement in PC for newly diagnosed advanced GI cancer patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".