The outcome of outpatient palliative care service at Yonsei Cancer Center, a tertiary cancer center in Korea.
Bibliographic record
Abstract
131 Background: The importance of palliative care in cancer patients continues to be emphasized and studies are proving its importance. Several studies proved the improvement of quality of life in advanced cancer patients. The efficacy of symptom control based on outpatient palliative care service has not yet been reported in Korea. The objective of this study is to review the outcome of outpatient palliative care service at Yonsei Cancer Center, a tertiary cancer center in Korea. Methods: We retrospectively reviewed 155 cancer patients who used outpatient clinic at Yonsei Cancer Center in Korea between April 2014 and December 2014. Symptom severity was measured by modified Korean version of Edmonton Symptom Assessment System. Twelve symptoms were assessed: pain, fatigue, nausea, depression, anxiety, drowsiness, dyspnea, sleep disorder, anorexia, constipation, wellbeing, financial distress. Higher score means worse symptom. ESAS scores at baseline and follow-up assessments were analyzed. Results: The 155 patients had following characteristics: female 52.3%, median age 65 years (range 58-75), Hepatobiliary-pancreatic cancer and lung cancer patients accounted for the largest portion (n = 37, 23.9%; n = 36, 23.2% respectively). Most patients were Eastern Cooperative Oncology Group performance status 1 (n = 28, 18.1%) or 2 (n = 24, 15.5%). Ninety-two (59.4%) patients were referred to the palliative care team after anti-cancer treatments were all finished. Overall the symptoms did not change significantly from baseline to 2 consecutive follow up assessment except anorexia ( p value = 0.0195). Patients who were on active anticancer treatment had tendency of higher ESAS score than those finished with the anticancer treatment. However, all symptoms except nausea were not statistically significant. Conclusions: Most patients in this study did not have severe symptom scores that would show the differences of the symptom changes. Nevertheless, although not statistically significant, we found that patients on active anticancer treatment had higher symptom burden than those who were finished with the anticancer treatment. More meticulous symptom management is necessary to improve the symptom control.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".