QOL-46. LATE EFFECTS CARE FOR CHILDHOOD BRAIN TUMOUR SURVIVORS: A QUALITY IMPROVEMENT PROJECT
Bibliographic record
Abstract
Abstract BACKGROUND Childhood and adolescent brain tumor survivors are at risk for considerable late morbidity and mortality from their disease and the treatment they receive. Surgery, chemotherapy, radiation therapy and tumor location all have the potential to impact the physical, psychological, functional and social health of these survivors. Comprehensive late effects care may mitigate these risks, but the necessary elements of this care model is unclear. We describe a quality-improvement initiative to improve the long-term follow-up (LTFU) care provided to brain tumour survivors at the McMaster Children’s Hospital. METHODS An anonymous needs assessment circulated to health providers was used to evaluate the LTFU practices. Utilizing this feedback as well as the LTFU guidelines from the Children’s Oncology Group a care plan was made for these survivors. RESULTS 17 of 33 (52%) health care staff responded to the survey, this included 70% physicians or nurse practitioners, and 30% nurses and allied health staff. Improvements suggested included consistent inclusion of additional care providers (i.e. social work, dietitians, endocrinology) reported by 76%, as well as a need for improved patient education and surveillance for late effects of therapy. CONCLUSION Treatment summaries with surveillance care plans and LTFU resources were created for all survivors of childhood brain tumours at risk of treatment-related complications. Late effects counselling with distribution of these materials is ongoing as part of this quality improvement initiative. To provide comprehensive management, a neuro-oncology specific late effects programs with multi-disciplinary support is essential for the care of brain tumour survivors.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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".