Prognosis does not change the landscape: palliative home care clients experience high rates of pain and nausea, regardless of prognosis
Bibliographic record
Abstract
BACKGROUND: Most individuals who typically receive palliative care (PC) tend to have cancer and a relatively short prognosis (< 6 months). People with other life-limiting illnesses can also benefit from a palliative care approach. However, little is known about those who receive palliative home care in Ontario, Canada's largest province. To address this gap, the goal of this project was to understand the needs, symptoms and potential differences between those with a shorter (< 6 months) and longer prognosis (6+ months) for individuals receiving PC in the community. METHODS: A cross-sectional analysis was conducted using interRAI Palliative Care (interRAI PC) assessment data collected between 2011 and 2018. Individuals with a shorter prognosis (< 6 months; n = 48,019 or 64.1%) were compared to those with a longer prognosis (6+ months; n = 26,945) across several clinical symptoms. The standardized difference (stdiff), between proportions, was calculated to identify statistically meaningful differences between those with a shorter and longer prognosis. Values of the stdiff of 0.2 or higher (absolute value) indicated a statistically significant difference. RESULTS: Overall, cancer was the most prevalent diagnosis (83.2%). Those with a shorter prognosis were significantly more likely to experience fatigue (75.3% vs. 59.5%; stdiff = 0.34) and shortness of breath at rest (22.1% vs. 13.4%; stdiff = 0.23). However, the two groups were similar in terms of severe pain (73.5% vs. 66.5%; stdiff = - 0.15), depressive symptoms (13.2% vs. 10.7%; stdiff = 0.08) and nausea (35.7% vs. 29.4%; stdiff = 0.13). CONCLUSIONS: These results highlight the importance of earlier identification of individuals who could benefit from a palliative approach to their care as individuals with a longer prognosis also experience high rates of symptoms such as pain and nausea. Providing PC earlier in the illness trajectory has the potential to improve an individual's overall quality of life throughout the duration of their illness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".