Screening for supportive care services in the neuro-oncology patient: A quality improvement project.
Bibliographic record
Abstract
355 Background: On average, 80 to 85% of patients diagnosed with brain tumors qualify for supportive care under the American Society of Clinical Oncology (ASCO) recommendations. The National Comprehensive Cancer Network (NCCN) recommends routine screening, yet in our experience only 39% are screened in-clinic and 8.7% qualifying are referred. Evidence within the literature suggests consistent patient symptom screening and reflexive supportive care referral leads to improved quality of life and treatment plan adherence, and reduced symptom burden, healthcare costs, and less aggressive end-of-life care. This QI/IS project sought to improve quality of life through increased symptom screening via the Edmonton Symptom Assessment Scale (ESAS) and initiation of supportive care referral in Neuro-Oncology patients. Methods: Institutional Review Board (IRB) appraisal deemed this project non-regulatory research. A retrospective chart review assessed pre-implementation consistency of patients screened using the ESAS tool, the number indicating symptom burden and supportive care qualification, and if referral was placed. The Plan-Do-Study-Act (PDSA) Framework guided implementation and process evaluation. Interventions focused on improving ESAS use to identify patients who would benefit from supportive services and decreasing barriers to supportive care such as supportive care misinformation, screening burden, and documentation. From October 2021 to March 2022, data collection included the patient demographics ethnicity, age, diagnosis, and encounter date, as well as patient ESAS scores and supportive care referrals placed. Results: Over a five-month implementation period, 357 ESAS symptom burden tools were completed and documented out of 378 patient encounters in the neuro-oncology outpatient clinic. Clinic ESAS completion rates increased from 39.3% to 94.4%. Symptom burden qualifying for supportive care referral improved in 18% (N = 66), down from 31.2% (N = 102) of patients. Of patients qualifying, referrals increased from 4.9% (N = 5) to 10.7% (N = 7). Conclusions: Consistent use of a patient screening process led to an increase in the number of identified patients with significant symptom burden and referral for supportive care services, meeting all project goals. Initially, qualified rates increased with improved screening processes and clinic education, then steadily decreased to indicate better symptom control. This suggests a process capable of meeting the screening needs of oncology patients and minimal resource needs to maintain long-term sustainability. Future projects aimed at supportive care access and resource availability are recommended.[Table: see text]
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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 | 0.043 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".