Effect of Edmonton Symptom Assessment Scale (ESAS) on symptom data collection in a radiation oncology department.
Bibliographic record
Abstract
83 Background: ESAS has been used to examine quality of life and symptom burden of patients undergoing cancer treatment. The purpose of this study is to examine attitudes towards ESAS among patients in a Radiation Oncology clinic in conjunction with the perspective of cancer care professionals, to establish ideal implementation of this tool to improve patient care. Methods: Routine use of ESAS in a single Radiation Oncology Department was initiated in July 2015. Six months after implementation, an anonymous, electronic survey was administered to 50 healthcare providers within this department, including attending physicians, resident physicians, advanced practice nurses, physician assistants, and registered nurses. The survey collected information regarding the value of ESAS with regards to patient care, the numerical value at which an intervention is made, which clinical interventions had been implemented due to patient-reported scores on ESAS, which patient populations benefit from ESAS administration, and how frequently ESAS should be administered. Closed and open questions were included. Results: Out of 50 providers, 36 completed the survey. Of these, 31 reported finding ESAS useful. The most common intervention was questioning the patient further about symptoms (29/36.) ESAS data are being reviewed by clinical teams and stored as part of the patient’s medical record in order to compile longitudinal data. An anonymous paper survey is currently being administered to 50 patients at the end of their radiation treatment or at their first follow-up. The survey will collect information about how well symptoms are being communicated with the clinical team, if symptoms should be added to ESAS, how often ESAS should be administered, which specific clinical interventions were provided due to ESAS, and if ESAS improved the overall patient experience. Conclusions: Our survey from the clinical team supports that ESAS is a useful modality to assess patient symptoms and to improve management for patient symptoms effectively. Our ongoing patient survey will validate these findings. These two surveys will be used to improve systematic collection of symptom data for radiation oncology patients.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".