MétaCan
Menu
Back to cohort
Record W2258640840 · doi:10.1093/nop/npv027

Glioblastoma in the elderly: making sense of the evidence

2015· article· en· W2258640840 on OpenAlexaff
Matthew Mason, Normand Laperrière, Wolfgang Wick, David A. Reardon, Annika Malmström, Elizabeth Hovey, Michael Weller, James Perry

Bibliographic record

VenueNeuro-Oncology Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineGlioblastomaRadiation therapyTemozolomideOncologyChemotherapyMethyltransferaseInternal medicineEvidence-based managementRandomized controlled trialEvidence-based practiceIntensive care medicineMethylationPathologyAlternative medicineCancer researchDNA

Abstract

fetched live from OpenAlex

Abstract Glioblastoma is a highly malignant neoplasm, notorious for its poor prognosis. The median age of diagnosis is 64 years, with an increasing number of patients diagnosed over the age of seventy. Managing elderly patients with this condition is challenging. Management pathways may include surgery, radiotherapy, chemotherapy, and best supportive care. Many clinical trials in oncology exclude elderly patients, including some of those for malignant brain tumors, leaving less evidence to guide treatment in these patients. Recent advances in molecular diagnostics and biomarkers, such as 06-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status, may help guide optimal treatment selection. Focusing on available randomized data, this review provides a practical overview of the evidence for treating newly diagnosed glioblastoma in the elderly, including management recommendations.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
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 science0.0000.000
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 head0.081
GPT teacher head0.383
Teacher spread0.302 · 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.

Study designNot applicable
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

Citations27
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueNeuro-Oncology PracticeSame topicGlioma Diagnosis and TreatmentFrench-language works237,207