Bibliographic record
Abstract
The evidence base for palliative care is heavily skewed toward patients with cancer, despite the fact that there are twice as many patients with palliative care needs and noncancer illness. This thesis seeks to establish the evidence for clinical practice and policy development for palliative care programs to improve end-of-life care. The first study was a systematic review and meta-analysis of randomized clinical trials of patients with primarily noncancer illness. We found that receipt of palliative care, compared with usual care, was significantly associated with less acute healthcare use and modestly lower symptom burden, and no significant difference in quality of life. The second study measured the association between newly initiated palliative care in the last 6 months of life, healthcare use and location of death in a cohort of adults dying from noncancer illness; and compared these associations with those who die from cancer. We found that among those dying of chronic organ failure, palliative care was associated with a reduction in the rate of emergency department use, hospitalizations and ICU admissions. Palliative care was associated with increased rates of emergency department use and hospitalization in patients dying of dementia, which differed depending upon whether they lived in the community or in a nursing home. In our third study, we measured the association between physician rates of referral to palliative care and location of death in hospitalized adults with serious illness, which include patients dying of cancer and noncancer illness. We found that patients who were cared for by physicians with higher rates of referral to palliative care were less likely to die in hospital and more likely to die at home. Standardizing referral to palliative care may help reduce physician-level variation in referral as a barrier to access. Collectively, these thesis findings highlight the potential benefits of palliative care in patients with select noncancer illness and identify further knowledge gaps for other common noncancer illnesses. Scaling existing palliative care to increase access through sustained investment in physician training and current models of collaborative palliative care may improve end-of-life care, which have significant implications for health policy.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".