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Record W2085526863 · doi:10.1186/bcr2312

Developing a prediction model for benefit from fulvestrant in heavily pretreated metastatic breast cancer patients

2009· article· en· W2085526863 on OpenAlexaff
Eitan Amir, OC Freedman, Stephen Chia, Teresa M. Petrella, Susan Dent, K. Tonkin, I. Ahmad, Daniel Rayson, George Dranitsaris, M. Clemons

Bibliographic record

VenueBreast Cancer Research · 2009
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsSaskatoon Medical ImagingQueen Elizabeth II Health Sciences CentreSunnybrook HospitalBC Cancer AgencyPrincess Margaret Cancer Centre
FundersAstraZeneca
KeywordsFulvestrantSurgical oncologyMedicineBreast cancerMetastatic breast cancerOncologyInternal medicineCancerEstrogen receptor

Abstract

fetched live from OpenAlex

Fulvestrant use in heavily pretreated patients with metastatic breast cancer is associated with highly variable responses. The present study aimed to characterize the benefit of fulvestrant therapy and to develop a prediction model for clinical benefit in this setting.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.101
GPT teacher head0.414
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 designSimulation or modeling
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

Citations0
Published2009
Admission routes1
Has abstractyes

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