ANALYSIS OF COMMON SYMPTOMS USING THE EDMONTON SYMPTOM ASSESSMENT SYSTEM IN TERMINALLY ILL CANCER PATIENTS RECEIVING PALLIATIVE CARE AT A TERTIARY CARE CENTER OF NEPAL
Bibliographic record
Abstract
Background: The Edmonton Symptom Assessment System (ESAS) is a reliable tool to assess the severity of Symptom over time. It evaluates nine symptoms commonly experienced by patients with cancer and other advanced illness. The aim of this study is to find the prevalent symptoms, intensity and prognostic significance of common symptoms in cancer patients. Methods: This prospective cross-sectional study enrolled 110 patients with terminal cancer receiving palliative care admitted at clinical oncology department of Bir Hospital. Patients providing informed written consent were advised to complete ESAS questionnaire within 24 hours of hospital admission. Data entry and analysis done in Microsoft Excel Version 2013. Frequency distributions, percentages, means, and standard deviations of various symptoms were analyzed. Scatter diagram was prepared to evaluate the time trend of all nine ESAS items toward death. Results: One hundred ten patients (mean age 53.76 ± 10.63 years, 70 female and 40 male) completed ESAS score questionnaire. The most common symptom experienced was poor well-being 97(88.18%), followed by tiredness 91(82.72%) and lack of appetite 88(80%). Most severe symptoms were poor well-being with a mean score of 5.27 ± 3.08, followed by tiredness (3.55 ± 2.46), pain (3.24 ± 2.61) and lack of appetite (3.15 ± 2.53) and all the symptoms tend to deteriorate towards end of life. Conclusions: Edmonton Symptom Assessment System (ESAS) can be used easily to assess common symptoms and their intensity in cancer patients which help to provide specific symptom directed treatment and care.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".