Effective routine electronic symptom screening and use of evidence to improve cancer symptom management in Ontario, Canada.
Bibliographic record
Abstract
e20507 Background: Improving symptom management is a critical component of high quality care. Cancer Care Ontario (CCO) is working to improve through a standardized approach to symptom screening and assessment that leverages electronic tools along the cancer journey. Methods: CCO has developed a web-based application that allows patients to report their symptoms using the Edmonton Symptom Assessment System (ESAS). ESAS is a validated screening tool that asks patients to rate the severity of nine common cancer symptoms. The use of ESAS promotes a common language among patients and providers and across care settings. To support clinicians in determining follow-up to scores, CCO has developed symptom management guides. The guides represent current evidence and best practices and are available in various formats to help the care team assess and manage a patient’s cancer-related symptoms. Results: Since the program’s introduction in 2007, over 1.6 million ESAS screens have been performed across the province. In November 2012, over 53% of all Regional Cancer Centre patients were screened representing over 23,000 patients and nearly 35,000 ESAS screens. Symptom assessments collected from 45,000 cancer patients revealed that 75% of patients reported fatigue as a concern, 57% reported anxiety and 53% reported pain. Patients have indicated that they value this approach. Results from a 2012 survey of 3,320 patients show that 93% thought that indicating their symptom severity is important as it helps their health care providers know how they are feeling. Evidence from chart audit reviews demonstrates that symptom screening is linked to higher rates of documented clinical interventions but further research is needed to analyze its impact on outcomes. Conclusions: Lessons learned include the importance of leadership at all levels, clinician engagement in change, point of care decision support tools and the importance of engaging patients in the management of their symptoms. Patients are overwhelmingly in support of this approach to cancer symptom screening and their involvement is critical to its expansion across all settings of 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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".