The use of a palliative care tool in a community private practice.
Bibliographic record
Abstract
95 Background: Although symptom assessment is a routine part of oncology care, data from the Michigan Oncology Quality Consortium (MOQC) showed variation in individual practice (Health Affairs 31:718, 2012). As part of the MOQC Palliative Care Demonstration Project, we implemented the Edmonton Symptom Assessment System(ESAS) in a private oncology clinic. ESAS is a valid and reliable assessment tool that evaluates nine common symptoms experienced by cancer patients. Our target population was patients with active cancer undergoing chemotherapy. Methods: Initial implementation focused on patients of only one of the practice’s physicians. Symptoms rated >3 were considered symptomatic and were addressed by the physician. To monitor overall performance, a practice profile was compiled from the individual ESAS results. For symptoms with the greatest severity and incidence, targeted resources were developed and integrated in new electronic medical record templates and educational sessions with patients. Results: Managing change incrementally with weekly reassessment of implementation problems was effective. Use of the ESAS tool allowed for a focused discussion of the patient symptomatology and lead to better efficiency for the physician. Understanding the symptom burden of the patient population and implementing practice wide interventions helped to reduce the symptom burden at an individual patient level. Conclusions: The ease of use of the ESAS tool makes it highly successful in the private oncology practice setting. Profiling the symptom burden at a practice level facilitates targeted improvements and monitoring of performance over time. [Table: see text]
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".