Palliative Care Consultation and Aggressive Care at End of Life in Unresectable Pancreatic Cancer
Bibliographic record
Abstract
Background: Palliative care (pc) consultation has been associated with less aggressive care at end of life in a number of malignancies, but the effect of the consultation timing has not yet been fully characterized. For patients with unresectable pancreatic cancer (upcc), aggressive and resource-intensive treatment at the end of life can be costly, but not necessarily of better quality. In the present study, we investigated the association, if any, between the timing of specialist pc consultation and indicators of aggressive care at end of life in patients with upcc. Methods: This retrospective cohort study examined the potential effect of the timing of specialist pc consultation on key indicators of aggressive care at end of life in all patients diagnosed with upcc in Nova Scotia between 1 January 2010 and 31 December 2015. Statistical analysis included univariable and multivariable logistic regression. Results: In the 365 patients identified for inclusion in the study, specialist pc consultation was found to be associated with decreased odds of experiencing an indicator of aggressive care at end of life; however, the timing of the consultation was not significant. Residency in an urban area was associated with decreased odds of experiencing an indicator of aggressive care at end of life. We observed no association between experiencing an indicator of aggressive care at end of life and consultation with medical oncology or radiation oncology. Conclusions: Regardless of timing, specialist pc consultation was associated with decreased odds of experiencing an indicator of aggressive care at end of life. That finding provides further evidence to support the integral role of pc in managing patients with a life-limiting malignancy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".