Exploring the Influence of the Edmonton Symptom Assessment System Implementation in Palliative Care Patients: A Systematic Review
Bibliographic record
Abstract
One of the most essential elements of providing high-quality palliative care is good symptom control, which is guided through an assessment of the evolution of the patient's level of distress over time. The Edmonton Symptom Assessment System (ESAS) was created to measure diverse symptoms in palliative care patients. It is a user-friendly, validated, and reliable multi-item instrument. Globally, ESAS is extensively used to direct daily clinical care and foster communication across multidisciplinary teams, ensuring coordinated patient management, facilitating referrals to specialized programs, and evaluating the quality of care. In this systematic review, we aim to evaluate the influence of using ESAS on symptom assessment and control among palliative care patients and settings. We have employed both manual and electronic search strategies among databases to determine relevant studies. This systematic review included original studies published between 2013 and 2023 that implemented the ESAS as part of palliative care for adult patients with terminal illness or advanced disease. Studies recruiting pediatric patients, case reports with limited sample sizes and no descriptive statistics, and nonhuman or laboratory studies were excluded. The ROBINS-I (Risk Of Bias In Non-randomized Studies-of Interventions) tool was used to assess the quality of the included studies, which assessed the methodological quality and potential risk of bias in non-randomized clinical studies. We included eight studies that recruited a total of 3184 patients. Fatigue followed by pain were the symptoms with the highest score on ESAS among all the studies, while the lowest score was recorded for nausea and dyspnea in most of the studies. ESAS scores showed improvement in the follow-up visits. Two of the included studies reported satisfaction with the utilization of the ESAS tool. As stated in our results, it can improve the overall quality of life for patients receiving palliative care by assisting healthcare professionals, family members, and caregivers in methodically assessing and treating symptoms over time. By fostering better communication among care teams and involving caregivers in the process, the use of ESAS promotes a more patient-centered approach to care, ensuring that both the patient's needs and the perspectives of their loved ones are considered.
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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 | 0.019 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".