SP680ASSESSING SYMPTOM ASSESSMENT AND MANAGEMENT PROCESSES FOLLOWING THE ADMINISTRATION OF A PATIENT-REPORTED OUTCOME MEASURE TO HEMODIALYSIS PATIENTS IN ONTARIO
Bibliographic record
Abstract
INTRODUCTION: People receiving hemodialysis commonly experience symptoms that healthcare providers may not routinely assess and/or manage. To address this gap in hemodialysis care, the Ontario Renal Network tested a standardized approach to symptom screening, assessment, and management using a patient-reported outcome measure, the Edmonton Symptom Assessment System Revised: Renal (ESAS-r:Renal). The objective of this study is to assess the results of ESAS-r:Renal administration on symptom assessment and management processes. METHODS: A total of 1,459 patients from 8 dialysis units in Ontario, Canada were included and completed the ESAS-r:Renal every 4 to 6 weeks. We audited randomly sampled charts, stratified by symptom severity, for six symptoms (anxiety, depression, itching, tiredness, pain, shortness of breath). Data was abstracted on whether the symptoms were discussed and what management approach was utilized. RESULTS: In total, 1,207 ESAS-r:Renal screens were reviewed between June-August 2018. Of these, 66% of screens had a documented assessment conversation (44% were symptom specific and 22% were general symptom conversations) and 50% had a documented symptom management approach. The proportion of documented assessment conversations and symptom management approaches increased with symptom severity. The most common documented symptom management approaches were patient education and lifestyle changes (25%), continuation of ongoing interventions (24%), new medications or prescriptions (13%), and referrals to allied health (5%). In 17% of screens, patients declined the symptom management approach recommended by their healthcare provider. CONCLUSIONS: Documented assessment conversations and management approaches increased with increasingly severe self-reported symptoms on the ESAS-r:Renal. The effects of these conversations and management on patient experience, symptom burden, and health resource utilization require further study.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".