The benefits of early integration of palliative care as a part of standard outpatient oncology care.
Bibliographic record
Abstract
18 Background: It is well known that palliative care (PC) services are skillful in aligning patients' goals of care with healthcare team’s plan of care and treating symptoms related to an advanced disease. The culture of PC is now focusing efforts upstream since earlier interaction fosters more thoughtful discussion of goals and more effective symptom management. Decreased or late access to PC services has traditionally been due to physician factors, including inaccuracies in prognostication, personal bias and lack of awareness of PC mission. With this understanding, we have consciously constructed a robust, standardized, formalized process in the oncology setting for patient identification and automatic referral to the outpatient PC clinic as a part of usual care to diminish the influence of oncologists. Methods: Due to the wide geographic area of our population, there are five ½ day outpatient PC clinics, two of which are embedded in outpatient oncology centers at two medical centers. These are staffed by an MD and RN, with option of chaplain and social work interventions. The Edmonton Symptom Assessment Scale serves as our tool to monitor patient’s clinical course. We have created a reproducible, patient selection and outreach process to make PC as part of ‘normal’ cancer care. Results: 330 oncology patients were seen over a 20 month period. Of these only 8 PC clinic patients received chemotherapy in the last 30 days prior to death vs 153 patients who were not seen in PC clinic. Within the last 14 days of life no patients who were seen in PC clinic received chemotherapy vs 54 patients who were not seen in clinic. This compares well to the national average of 10%. 55% were referred to hospice compared to the national average of only 11%. The average LOS for hospice was 50.8 days compared to the national average of 40.6 days. ER visits decreased by 15% and hospital admissions decreased by 63% for patients with PC clinic encounters. Subjectively, patient’s symptoms improved by 75%. Conclusions: These upstream encounters facilitate more thoughtful medical decision making for patients with terminal cancer diagnosis, improving quality of care and QOL and positively affecting hospital utilization.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".