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Record W2966337131 · doi:10.3389/fonc.2019.00797

The Impact of Targeted Therapy on Intracranial Metastatic Disease Incidence and Survival

2019· article· en· W2966337131 on OpenAlexafffund
Anders W. Erickson, Sunit Das

Bibliographic record

VenueFrontiers in Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
FundersUniversity of Toronto
KeywordsMedicineTargeted therapyRadiation therapyIncidence (geometry)DiseaseOncologyInternal medicineCancerSystemic therapyIntensive care medicineBreast cancer

Abstract

fetched live from OpenAlex

Intracranial metastatic disease (IMD) is a common and severe complication of primary cancers. Current treatment options for IMD include surgical resection and radiation therapy, although there has been recent interest in targeted therapy in the management of IMD. As of yet, insufficient data exist to support the recommendation of targeted therapies in the treatment of IMD. Paradoxically, targeted therapy has been hypothesized to play a role in the development of IMD in patients with primary cancers. This is based on the observations that patients who receive targeted therapy for primary cancer experience prolonged survival, and that prolonged survival has been associated with increased incidence of IMD. Few data exist to clarify if treatment of primary cancers with targeted therapies influences IMD incidence. Here, we discuss the role of targeted therapy in IMD management, review the current literature on IMD incidence and targeted therapy use in primary cancer, and propose the need for future studies to inform physicians in choosing treatment options and counseling patients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.329
Teacher spread0.314 · 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 designObservational
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

Citations11
Published2019
Admission routes2
Has abstractyes

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