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Record W3134191979 · doi:10.3171/2020.10.jns203257

Ventricular size determination and management of ventriculomegaly and hydrocephalus in patients with diffuse intrinsic pontine glioma: an institutional experience

2021· article· en· W3134191979 on OpenAlexaff
Adriana Fonseca, Palma Solano‐Páez, Vijay Ramaswamy, Uri Tabori, Annie Huang, James M. Drake, Derek S. Tsang, Normand Laperrière, Ute Bartels, Abhaya V. Kulkarni, Éric Bouffet

Bibliographic record

VenueJournal of neurosurgery · 2021
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsVentriculomegalyMedicineHydrocephalusAsymptomaticPopulationSurgeryRadiologyProgression-free survivalChemotherapyPregnancy

Abstract

fetched live from OpenAlex

OBJECTIVE: There is no consensus on the optimal clinical management of ventriculomegaly and hydrocephalus in patients with diffuse intrinsic pontine glioma (DIPG). To date, the impact on survival in patients with ventriculomegaly and CSF diversion for hydrocephalus in this population remains to be elucidated. Herein, the authors describe their institutional experience. METHODS: Patients diagnosed with DIPG and treated with up-front radiation therapy (RT) at The Hospital for Sick Children between 2000 and 2019 were identified. Images at diagnosis and progression were used to determine the frontal/occipital horn ratio (FOR) as a method to measure ventricular size. Patients with ventriculomegaly (FOR ≥ 0.36) were stratified according to the presence of symptoms and categorized as follows: 1) asymptomatic ventriculomegaly and 2) symptomatic hydrocephalus. For patients with ventriculomegaly who did not require CSF diversion, post-RT imaging was also evaluated to assess changes in the FOR after RT. Proportional hazards analyses were used to identify clinical and treatment factors correlated with survival. The Kaplan-Meier method was used to perform survival estimates, and the log-rank method was used to identify survival differences between groups. RESULTS: Eighty-two patients met the inclusion criteria. At diagnosis, 28% (n = 23) of patients presented with ventriculomegaly, including 8 patients who had symptomatic hydrocephalus and underwent CSF diversion. A ventriculoperitoneal shunt was placed in the majority of patients (6/8). Fifteen asymptomatic patients were managed without CSF diversion. Six patients had resolution of ventriculomegaly after RT. Of 66 patients with imaging at the time of progression, 36 (55%) had ventriculomegaly, and 9 of them required CSF diversion. The presence of ventriculomegaly at diagnosis did not correlate with survival on univariate analysis. However, patients with symptomatic hydrocephalus at the time of progression who underwent CSF diversion had a survival advantage (p = 0.0340) when compared to patients with ventriculomegaly managed with conservative approaches. CONCLUSIONS: Although ventriculomegaly can be present in up to 55% of patients with DIPG, the majority of patients present with asymptomatic ventriculomegaly and do not require surgical interventions. In some cases ventriculomegaly improved after medical management with steroids and RT. CSF diversion for hydrocephalus at the time of diagnosis does not impact survival. In contrast, our results suggest a survival advantage in patients who undergo CSF diversion for hydrocephalus at the time of progression, albeit that advantage is likely to be confounded by biological and individual patient factors. Further research in this area is needed to understand the best timing and type of interventions in this population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.233
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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