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Record W2728716594 · doi:10.21037/cco.2017.06.03

Glioblastoma in the elderly: initial management

2017· review· en· W2728716594 on OpenAlexaff
Fábio Ynoe de Moraes, Normand Laperrière

Bibliographic record

VenueChinese Clinical Oncology · 2017
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineTemozolomideOncologyInternal medicineAdjuvantRadiation therapyPopulationGlioblastomaCancer research

Abstract

fetched live from OpenAlex

Glioblastoma is the most common primary CNS malignancy and it is becoming more frequently diagnosed in the elderly population. Glioblastoma is associated with a dismal prognosis and remains a huge challenge for the neuro-oncology community. Surgical resection/biopsy is well defined as an important first approach in the care of this disease, for tumor diagnosis, molecular analysis and maximum resection. MGMT promoter methylation status has proved to be a useful indicator of whether single modality (RT or TMZ alone) or combined modality treatment may achieve better outcomes. Post-operative treatment options include: (I) hypofractionated radiotherapy (HRT) with concurrent and adjuvant temozolomide (TMZ) or (II) HRT alone (MGMT unmethylated patients); (III) TMZ alone (MGMT methylated patients) when combined modality is not feasible due to patient poor performance status or multiple comorbidities. Following the positive survival outcomes of the CCTG CE.6/EORTC 26062-22061 phase III trial which randomized newly diagnosed glioblastoma patients aged 65 or older to HRT (40 Gy/15 fractions) with concurrent and adjuvant temozolomide to HRT alone, combined modality therapy (CMT) with HRT with concurrent temozolomide as the initial post-surgical approach should be considered in patients well enough to have treatment. In meantime, future trials addressing new approaches are needed to improve outcomes in this fatal disease.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.239
GPT teacher head0.560
Teacher spread0.321 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2017
Admission routes1
Has abstractyes

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