NU-18NEWLY DIAGNOSED HIGH GRADE GLIOMA AND DIPG: COLLABORATIVE EFFORTS OF PEDIATRIC NEURO-ONCOLOGY AND PALLIATIVE CARE
Bibliographic record
Abstract
Although the idea of offering palliative care services early in the illness trajectory is not new, it can be a difficult introduction when caring for children with a cancer diagnosis. The time of a neuro-oncology diagnosis is often complex with acute onset of symptoms and hospitalization often including intensive care, with a high risk for post-operative or treatment complications. The risk of morbidity and mortality is high and symptom management can be complex making the early introduction of a palliative care team more accepted. Diagnosis of high grade glioma and DIPG are associated with poor outcomes and low overall survival. Between January 2013 and Dec 2015, 75 children under age 17 were diagnosed with brain or spinal cord tumors in our program with 4 high grade glioma and 8 DIPG diagnosis. In 11 of 12 cases, Neuro-oncology Nursing and Palliative care worked collaboratively to care for patients in the outpatient setting beginning within the initial outpatient visits. Utilizing a collaborative approach to care, patients and families had access to support and symptom management throughout their disease trajectory. The proportion of care provided by each subspecialty varied based on patient and family needs. In addition the transfer and support of care in the home community was well facilitated with the combined care model.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".