MétaCan
Menu
Back to cohort
Record W2899121209 · doi:10.3747/co.25.4153

Neoadjuvant Therapy for Breast Cancer: Updates and Proceedings From the Seventh Annual Meeting of the Canadian Consortium for Locally Advanced Breast Cancer

2018· article· en· W2899121209 on OpenAlexafffundvenueabout
Angel Arnaout, J. Lee, Karen A. Gelmon, Brigitte Poirier, Fang‐I Lu, Mohamed Akra, Jean-François Boileau, K. Tonkin, H. Li, C. Illman, C. Simmons, Debjani Grenier

Bibliographic record

VenueCurrent Oncology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsJewish General HospitalHealth Sciences CentreCancerCare ManitobaSunnybrook Health Science CentreJuravinski Cancer CentreOttawa Hospital
FundersOntario Institute for Cancer ResearchGenomic HealthAstraZenecaPfizerAmgen
KeywordsBreast cancerMedicineSystemic therapyModalitiesMultidisciplinary approachRadiation therapyMedical physicsCancerOncologyInternal medicine

Abstract

fetched live from OpenAlex

Therapy for breast cancer involves a complex interplay of three main treatment modalities: surgery, systemic therapy, and radiation therapy. The Canadian Consortium for Locally Advanced Breast Cancer (LABC) was established with the goal to convene a strong multidisciplinary team of breast oncology clinicians and scientists who are dedicated to the advancement of LABC research and treatment, with a vision to drive progress through increased collaboration across disciplines and throughout Canada. The most recent meeting in May 2017 highlighted the latest evidence and literature about the optimal use of neoadjuvant systemic therapy in breast cancer. There is a need for increased clinical and scientific collaboration and the development of guidelines for the use of emerging treatment strategies. The interactive meeting sessions fostered unique opportunities for academic debate and nurtured collaboration between the attendees.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.991

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 studies0.0000.001
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 head0.024
GPT teacher head0.337
Teacher spread0.313 · 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

Citations23
Published2018
Admission routes4
Has abstractyes

Explore more

Same venueCurrent OncologySame topicBreast Cancer Treatment StudiesFrench-language works237,207