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Record W2108301172 · doi:10.3171/foc.2006.20.4.12

Treatment options for glioblastoma

2006· review· en· W2108301172 on OpenAlexaboutno aff
Marc C. Chamberlain

Bibliographic record

VenueNeurosurgical FOCUS · 2006
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsTemozolomideMedicineOncologyRadiation therapyInternal medicineGlioblastomaClinical trialAdjuvantPseudomonas exotoxinCancerChemotherapyCancer researchBiology

Abstract

fetched live from OpenAlex

Following the seminal trial conducted by the European Organisation for Research and Treatment of Cancer (EORTC) and the National Cancer Institute of Canada (NCIC), concurrent temozolomide and radiotherapy has become the new standard of care for patients with newly diagnosed glioblastoma multiforme (GBM). Investigation of emerging therapies (which are now used as salvage therapy) such as small-molecule inhibitors (for example, epidermal growth factor receptor inhibitors) and convection-enhanced delivery (CED) of targeted toxins (for example, interleukin-13/pseudomonas exotoxin) is likely to build on the EORTC/NCIC treatment platform and will, it is hoped, improve survival rates in patients with GBM. The majority of adjuvant Phase I and II trials being conducted by the brain tumor consortia are based on the EORTC/NCIC treatment platform and have added a targeted therapy in an effort to find a promising synergistic treatment. Furthermore, researchers in the consortia are continuing to explore treatments for recurrent GBM, not otherwise eligible for local therapies, such as CED. The treatments under study include novel cytotoxic chemotherapy as well as small-molecule inhibitors; these are being assessed in a variety of Phase I or II trials.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.058
GPT teacher head0.359
Teacher spread0.301 · 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 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

Citations56
Published2006
Admission routes1
Has abstractyes

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