Population-based standardized symptom screening: Cancer Care Ontario’s Edmonton Symptom Assessment System and performance status initiatives.
Bibliographic record
Abstract
56 Background: The goal of the collaborative is to improve the quality and consistency of physical and emotional symptom management across the cancer journey. Objectives are: (a) promote the adoption of electronic symptom assessment using a standardized tool and (b) increase the clinical use of evidence based guidelines to effectively manage patient identified symptoms. Methods: The actions taken for this initiative are to manage cancer symptoms through a patient reported measurement tool; improve the quality of symptom management through the uptake of symptom management guides and algorithms for care; and drive improvement through the adoption of an electronic symptom assessment platform The following aims were established for this work: (1) Aim for symptom screening and assessment (70% of ambulatory cancer clinic patients are screened for symptom severity using ESAS at least once/month) (2) aim for symptom management (evidence from chart audits show intervention as per evidence based guidelines for patients reported symptom scores) (3) aim for patient satisfaction (90% of target population indicates that their health care team took their scores into consideration when developing a care plan) and (4) aim for evidence of use (90% of patients state that their doctor or nurse spoke with them about their symptom screen). Results: 60% of cancer patients are screened each month representing over 28,000 people. Six of fourteen cancer regions are above the provincial target of 70%, with some close to 90%. 92% of patients felt ESAS was important to complete to help health care providers know how they are feeling. Conclusions: Cancer Care Ontario has been able to drive improvements in symptom management through the implementation of system wide electronic symptom assessment. For other jurisdictions interested in adopting this approach, the following areas are critical for success. (a.) Leadership at all levels of the system; (b.) clinical tools at the point of care; (c) engagement of patients in the design of care; (d) communications support to spread information to all stakeholders; and (e) using to data to drive performance improvement and accountability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".