DIPG-20. DETERMINATION AND MANAGEMENT OF HYDROCEPHALUS IN PATIENTS WITH DIPG, AN INSTITUTIONAL EXPERIENCE
Bibliographic record
Abstract
Abstract BACKGROUND The is no consensus in best practices for the management of hydrocephalus in patients with Diffuse Intrinsic Pontine Glioma (DIPG). To date, the impact on survival of hydrocephalus and Cerebro-Spinal Fluid (CSF) diversion in this population remains to be elucidated. Herein, we describe our institutional experience. METHODS Patients with a clinical and radiological diagnosis of DIPG were identified at the Hospital for Sick Children between 2000–2019. Images at diagnosis and at disease progression were assessed for hydrocephalus using the frontal-occipital ratio (FOR) method. Proportional hazard analyses were used to identify factors correlated with survival. RESULTS Eighty-nine consecutive patients diagnosed with DIPGs were treated at our institution. At diagnosis, 29% (n=26) of patients presented with hydrocephalus, seven patients underwent CSF diversion. Out of the remaining nineteen patients, n=6 had stable or improved hydrocephalus in follow-up scans, n=6 had persistent hydro and n=2 required CSF diversion at the time of disease progression. Seven did not undergo a follow-up scan. Out of sixty-five patients with imaging at the time of progression, fifty-five percent of patients (n=36) presented with hydrocephalus and ten of them required CSF diversion. On univariate analysis, the presence of hydrocephalus or CSF diversion at diagnosis and/or did not correlate with a survival advantage. CONCLUSIONS CSF diversion for the management of hydrocephalus in patients with DIPG does not impact survival and in some cases resolves spontaneously after the initiation of radiotherapy and steroids. This observation needs to be validated in a prospective cohort.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".