Integrating palliative care services in oncology clinics: An application of the Edmonton Symptom Assessment System (ESAS).
Bibliographic record
Abstract
168 Background: Research inpalliative care has shown improvements in overall survival, quality of life, symptom management, care satisfaction and reductions in the cost of care. Therefore, the American Society of Clinical Oncology has recommended early concurrent palliative care in patients with advanced cancer and with high symptom burden. Despite this recommendation, integrating palliative services at our NCI-designated cancer center has been challenging. The aims of this project were to quantitatively describe the symptom burden of patients in ambulatory oncology clinics; facilitate the establishment of an effective referral system by detecting discrepancies between symptom burden and referral practices; and improve the integration of palliative care services by implementing the Edmonton Symptom Assessment System (ESAS) tool into 5 of our oncology clinics. Methods: ESAS forms consist of 10 questions assessing patient symptom burden and quality of life. Total scores range from 0 to 100. This tool was distributed to patients at two breast, two gastrointestinal and the thoracic clinics at each visit. The provider reviewed the forms and decided if a palliative care referral was appropriate based on patient responses. The forms as well as referral decisions were entered into REDCap. Over a 5 month period, 607 patients completed the initial assessment and 430 follow up forms were collected, resulting in a total of 1,037 scores collected. Results: The mean ESAS score for all patient visits was 20.7 (SD = 18.7). Only 3.5% (n = 21) of all patients were initially referred to palliative care and 2.6% (n = 11) of patients were referred on follow up visits. Those with an initial referral had an initial mean score of 39.0 (SD = 19.0) and a mean follow up score of 31.9 (SD = 19.5). Conclusions: This project highlights the low palliative care consultation rate and the under-utilization of services by most oncologists at the cancer center despite using the ESAS tool. However, those who received a referral had lower ESAS scores at follow-up. We propose utilizing a trigger that would capture a preset percentage of patients who indicated scores reflective of high symptom burden and distress.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".