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Record W3092429340 · doi:10.1017/cjn.2020.224

Brain Metastases: A Modern Multidisciplinary Approach

2020· review· en· W3092429340 on OpenAlexaffvenue
Philip J. O’Halloran, Enrique Gutiérrez, Aristotle Kalyvas, Nilesh Mohan, Soha Atallah, Suneil K. Kalia, Barbara‐Ann Millar, Normand Laperrière, Tatiana Conrad, Gelareh Zadeh, Mark Bernstein, David Shultz, Paul Kongkham

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2020
Typereview
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsPrincess Margaret Cancer CentreToronto Western Hospital
Fundersnot available
KeywordsMultidisciplinary approachMedicineMedical physicsPolitical science

Abstract

fetched live from OpenAlex

Brain metastases (BM) are the most common intracranial neoplasm and represent a major clinical challenge across many medical disciplines. The incidence of BM is increasing, largely due to improvements in primary disease therapeutics conferring greater systemic control, and advancements in neuroimaging techniques and availability leading to earlier diagnosis. In recent years, the landscape of BM treatment has changed significantly with the advent of personalized targeted chemotherapies and immunotherapy, the adoption of focal radiotherapy (RT) for higher intracranial disease burden, and the implementation of new surgical strategies. The increasing permutations of options available for the treatment of patients diagnosed with BM necessitate coordinated care by a multidisciplinary team. This review discusses the current treatment regimens for BM as well as examines the salient features of a modern multidisciplinary approach.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.100
GPT teacher head0.345
Teacher spread0.245 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicBrain Metastases and TreatmentFrench-language works237,207