MétaCan
Menu
Back to cohort
Record W4376613623 · doi:10.1016/j.esmoop.2023.101409

220P Oral selective estrogen receptor degraders for metastatic hormone receptor-positive, HER2 negative breast cancer according to ESR1 mutation: A systematic review and meta-analysis of randomized control trials

2023· review· en· W4376613623 on OpenAlexaff
R. Novello, Miriam Lobo, M. Silveira Vilbert, Solange Moraes Sanches, Marcelle Goldner Cesca

Bibliographic record

VenueESMO Open · 2023
Typereview
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineMeta-analysisOncologyRandomized controlled trialEstrogen receptorBreast cancerSelective estrogen receptor modulatorMetastatic breast cancerFulvestrantCancerCochrane Library

Abstract

fetched live from OpenAlex

Oral selective estrogen receptor degraders (SERDs) are a promising treatment after disease progression on first-line endocrine therapy (ET) for hormone receptor-positive (HR+), HER2 negative advanced breast cancer (aBC) patients. This systematic review and meta-analysis of randomized clinical trials (RCTs) aimed to assess the efficacy of oral SERDS versus standard of care (SOC) ET according to ESR1 mutation (ESR1mut).

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)0.009
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0180.032
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.228
GPT teacher head0.490
Teacher spread0.263 · 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 designMeta-analysis
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueESMO OpenSame topicCancer Treatment and PharmacologyFrench-language works237,207