Home Care Use and Out-of-Hospital Death in Pancreatic Cancer Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
Objective: This study aimed to determine the factors associated with usage of home care, including palliative home care, in patients with pancreatic cancer in Ontario. In addition, this study attempted to investigate factors associated with early-onset palliative home care as well as the impact of home care services on survival and out-of-hospital death. Methods: The Ontario Cancer Registry (OCR) was used to identify and capture basic patient/cancer characteristics of index cases of pancreatic cancer diagnosed between April first, 2010 and March 31st, 2016. Patients that received home care were identified using the Home Care Database (HCD) and stratified into general, transition-to-palliative, and early-onset palliative home care. Logistic regressions were used to describe determinants of home care use and determinants of out-of-hospital death. Results: A total of 6888 pancreatic cancer patients met eligibility criteria for this study. A high proportion of patients (83.7%) received home care, including palliative home care (56.8%). In general, older patients (OR = 3.07) and those with more advanced malignancy (OR = 4.98) for stage 4 versus stage 1) had greater odds of receiving palliative home care. Patients receiving home care ( P < .01) and those residing in rural regions ( P < .01) had greater odds of out-of-hospital death. Conclusion: A large proportion of patients with pancreatic cancer are directed to home care and those that do are more likely to die outside of hospital. Age and stage at diagnosis are significant predictors of home care use. Differences exist in the healthcare experience of patients depending on if they receive home care services and the type of home care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".