Exploring the Definitions and Outcomes of Early Palliative Care Criteria in Individuals with Advanced Lung Cancer: A Multiple Method Study
Bibliographic record
Abstract
Background: In Ontario, about 51.9% of decedents have palliative care records in their last year, but only 1/5 access publicly funded home care. Early specialist palliative care may boost home service use and lower hospital visits. Early palliative care (EPC) has multiple definitions, and this thesis aims to identify these definitions, assess their applicability to health data, and analyze them using EOL outcomes. Objectives: This dissertation aimed to review and synthesize EPC definitions for individuals with life-limiting chronic illnesses (cancer and non-cancer) to inform criteria; establish consensus on defining EPC using administrative data for advanced cancer patients in Ontario; and examine associations between EPC criteria and quality indicators using ICES data. Methods: This dissertation was completed using multiple methods. For objective 1, a scoping review was conducted, following the Joanna Briggs Institute Methodology. Objective 2 used review results and a consensus method with pre-set criteria in a modified Delphi study. For objective 3, a population-based retrospective cohort study analyzed administrative data from ICES. Results: EPC definitions in literature vary, including initiation and implementation. A scoping review of 153 articles identified five EPC criteria categories: time-based, prognosis-based, location-based, treatment-based, and symptom-based. From these, five criteria applicable to lung cancer patients using ICES data were condensed into three: 1) time-from-index (disease to first palliative care), 2) time-before-death (care >3 months before death), and 3) first-care setting (outpatient). These were analyzed for associations with supportive and aggressive EOL indicators. Time-from-index (0-4 and 4-8 weeks) correlated with fewer aggressive and more supportive indicators. Time-before-death showed similar patterns, as did first-care setting EPC criteria. Conclusions: This dissertation shows variability in defining EPC but confirms its importance and link to better end-of-life quality indicators. Different time-based definitions serve different purposes, reflecting various aspects of care. Examining evidence-based definitions has highlighted the pros and cons of each approach, which is vital for analyzing EPC interventions. Ultimately, creating a standardized, patient-centred definition incorporating timing, location, and needs is key for equitable access and optimal outcomes.
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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 | 0.079 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".