Who Needs a Palliative Care Consult?: The Hamilton Chart Audit Tool
Bibliographic record
Abstract
Although palliative care services are becoming increasingly prevalent in acute care hospitals only a minority of patients who die in hospital or in the community have seen palliative care teams. There are large numbers of patients who might benefit from palliative care who are not receiving it. That said, identification of patients who are eligible for these services, and of those who would most benefit is problematic. Limitations in our ability to accurately predict prognosis as well as lack of universal agreement as to what constitutes a terminal illness, or "end of life" are important considerations. Another significant challenge faced by our health care systems is whether or not all "end-of-life" patients require specialized care by trained palliative care providers. Even if this were the ideal model of care, this would be unfeasible given the relatively small number of trained providers compared to the aging and dying population. Therefore it is critical that health care systems begin to standardize their approach to the identification of patients who are most in need of, and/or most likely to benefit from interventions by interdisciplinary palliative care teams. Institutions that are planning to develop new services, or expand their current services will require some method/tool to assess specific population needs at their site. The Hamilton Chart Audit (H-CAT) was developed at our institution to help identify potential palliative care needs of patients and their families. We report on development of the tool and use of the tool for a retrospective audit of 222 patients who died at our institution.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".