Team-based outpatient early palliative care: a complex cancer intervention
Bibliographic record
Abstract
BACKGROUND: Although the effectiveness of early palliative care for patients with advanced cancer has been demonstrated in several trials, there has been no detailed published description of an early palliative care intervention. METHOD: In this paper, we delineate the iterative conception and systematic evaluation of a complex intervention called team-based outpatient early palliative care (TO-EPC), and describe the components of the intervention. The intervention was developed based on palliative care theory, review of previous palliative care interventions and practice guidelines. We conducted feasibility testing and piloting of TO-EPC in a phase 2 trial, followed by evaluation in a large cluster randomised trial and qualitative research with patients and caregivers. The qualitative research informed the iterative refinement of the intervention. RESULTS: Four principles and four domains of care constitute a conceptual framework for TO-EPC. The main domains of care are: coping and support, symptom control, decision-making and future planning. The main principles are that care is flexible, attentive, patient-led and family-centred. The most prominent domain for the initial consultation is coping and support; follow-up visits focus on symptom control, decision-making to maximise quality of life and future planning according to patient readiness. Key tasks are described in relation to each domain. CONCLUSION: The description of our intervention may assist palliative care teams seeking to implement it, researchers wishing to replicate or build on it and oncologists hoping to adapt it for their 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".