MétaCan
Menu
Back to cohort

A single center experience in the management of progressive juvenile pilocytic astrocytoma

2022· article· en· W3154544210 on OpenAlexaff
Ieta Shams, Branavan Manoranjan, Rebecca Voth, Malavan Ragulojan, Olufemi Ajani, Blake Yarascavitch, Sheila K. Singh, Adam Fleming

Bibliographic record

VenueJournal of Neurosurgical Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of CalgaryUniversity of OttawaMcMaster University
Fundersnot available
KeywordsMedicinePsychological interventionSingle CenterPilocytic astrocytomaAstrocytomaRadiologyPediatricsSurgeryGliomaPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Juvenile pilocytic astrocytoma (JPA) typically follows an indolent clinical course. The first-line treatment for most JPAs is surgical resection. However, a gross total resection may not be feasible for deep-seated lesions and/or infiltrative tumors, leading to multimodal treatment approaches that may be complicated by patient age and tumor location. Despite the prevalence of pediatric JPAs, there is no single approach to treating progressive disease. METHODS: We investigated the multifaceted management of progressive JPAs through a retrospective analysis of JPAs treated at a single center over an 18-year period (1998-2016). All cases were categorized according to location, whether supratentorial or infratentorial, and for each case we calculated the number of interventions and the time between interventions. RESULTS: We identified a total of 40 JPAs, (11 supratentorial, 29 infratentorial). Total number of interventions among all supratentorial JPA patients was 21 (average 2 interventions/patient). The total number of interventions among infratentorial JPAs was 40 (average 1.4 interventions/patient). CONCLUSIONS: Treatment of progressive JPA is variable and may require numerous surgeries and adjuvant therapies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.059
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

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

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 head0.059
GPT teacher head0.329
Teacher spread0.270 · 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 teacher head, 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Neurosurgical SciencesSame topicGlioma Diagnosis and TreatmentFrench-language works237,207