QLTI-19. PEDIATRIC NEURO-ONCOLOGISTS NAVIGATING AN ADULT WORLD: A TALE OF TWO CENTERS
Bibliographic record
Abstract
Abstract BACKGROUND Adolescent and Young Adult Neuro-Oncology(AYA-NO) patients are a distinct cohort with an overlap of tumors found in childhood and adulthood. For adult patients with pediatric-type Central Nervous System tumors, optimal work-flow to manage these patients is unclear. METHODS We examine the work-flow of pediatric neuro-oncologists starting AYA-NO clinics within two high volume adult institutions: the University of Miami(Florida, USA) and the Princess Margaret Cancer Centre(Toronto, Canada). RESULTS Over 18 months, the AYA-NO clinics managed 167 patients total with several key differences emerging in both institutions between the pediatric and adult work-flows. Adult providers commonly have neurology backgrounds, while pediatric providers mostly have hematology/oncology backgrounds. In pediatrics, providers typically meet the patient inpatient before pathology results, adult providers meet their patients in outpatient settings once histopathology results are available. Outpatient management in pediatric oncology centers typically includes labs, infusion centers, provider rooms, and social workers in one area while adults, due to the larger volume, have these services on separate units. Chemotherapy related differences included the primary provider for inpatient admissions and the emphasis of adult institutions on outpatient administration when feasible. Psychosocial factors such as the variety of caregivers, increased opportunity for patient autonomy in decision-making, and the increased fragmentation in care magnifies the importance of providers forming a strong rapport with AYA-NO patients. End of life care is vastly different including the inability for adult patients to receive palliative treatment in hospice care and the availability medically-assisted death. CONCLUSION Pediatric neuro-oncologists building AYA-NO workflows within adult settings can provide unique expertise for AYA-NO patients with pediatric-type tumors. Growing AYA-NO clinics can benefit from allocating AYA CNS specific resources such as chemotherapy/targeted therapy provider education, a dedicated hospital/clinic space, chemotherapy nursing, a care coordinator, and a social worker with experience treating this patient population.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".