Prescreening of Patient-reported Symptoms using the Edmonton Symptom Assessment System (ESAS) in Outpatient Palliative Cancer Care
Bibliographic record
Abstract
Abstract Background: Although early palliative care is associated with a better quality of life and improved outcomes in end-of-life cancer care, the criteria of palliative care referral are still elusive. Methods: We collected patient-reported symptoms using the Edmonton Symptom Assessment System (ESAS) at the baseline, first, and second follow-up visit. The ESAS evaluates ten symptoms: pain, fatigue, nausea, depression, anxiety, drowsiness, dyspnea, sleep disorder, appetite, and wellbeing. A total of 71 patients were evaluable, with a median age of 65 years, male (62%), and the Eastern Cooperative Oncology Group (ECOG) performance status distribution of 1/2/3 (28%/39%/33%), respectively. Results: Twenty (28%) patients had moderate/severe symptom burden with the mean ESAS ≥5. Interestingly, most of the patients with moderate/severe symptom burdens (ESAS ≥5) had globally elevated symptom expression. While the mean ESAS score was maintained in patients with mild symptom burden (ESAS<5; 2.7 at the baseline; 3.4 at the first follow-up; 3.0 at the second follow-up; P =0.117), there was significant symptom improvement in patients with moderate/severe symptom burden (ESAS≥5; 6.5 at the baseline; 4.5 at the first follow-up; 3.6 at the second follow-up; P <0.001). Conclusions: Advanced cancer patients with ESAS ≥5 may benefit from outpatient palliative cancer care. Prescreening of patient-reported symptoms using ESAS can be useful for identifying unmet palliative care needs in advanced cancer patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".