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Record W2013614225 · doi:10.1016/s1470-2045(13)70308-5

Challenges relating to solid tumour brain metastases in clinical trials, part 2: neurocognitive, neurological, and quality-of-life outcomes. A report from the RANO group

2013· review· en· W2013614225 on OpenAlexaff
Nancy U. Lin, Jeffrey S. Wefel, Eudocia Q. Lee, David Schiff, Martin J. van den Bent, Riccardo Soffietti, John H. Suh, Michael A. Vogelbaum, Minesh P. Mehta, Janet Dancey, Mark E. Linskey, D. Ross Camidge, Hidefumi Aoyama, Paul D. Brown, Susan M. Chang, Steven N. Kalkanis, Igor J. Barani, Brigitta G. Baumert, Laurie E. Gaspar, F. Stephen Hodi, David R. Macdonald, Patrick Y. Wen

Bibliographic record

VenueThe Lancet Oncology · 2013
Typereview
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsWestern UniversityLondon Health Sciences CentreOntario Institute for Cancer ResearchQueen's University
FundersAmerican Academy of Clinical NeuropsychologyBreast Cancer Research Foundation
KeywordsNeurocognitiveQuality of life (healthcare)Clinical trialMedicineClinical study designReliability (semiconductor)Independence (probability theory)Medical physicsOncologyIntensive care medicinePsychologyPhysical therapyInternal medicineCognitionPsychiatryNursing

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.020
metaresearch head score (Gemma)0.037
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 score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.656
GPT teacher head0.564
Teacher spread0.093 · 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

Citations143
Published2013
Admission routes1
Has abstractno

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