MétaCan
Menu
Back to cohort
Record W3129342924 · doi:10.2217/cer-2020-0272

Matching-adjusted indirect comparison of palbociclib versus ribociclib and abemaciclib in hormone receptor-positive/HER2-negative advanced breast cancer

2021· article· en· W3129342924 on OpenAlexaff
Hope S. Rugo, Anja Haltner, Lin Zhan, Anh N. Tran, Eustratios Bananis, Becky Hooper, Debanjali Mitra, Chris Cameron

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2021
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsEVERSANA (Canada)
Fundersnot available
KeywordsMedicineFulvestrantPalbociclibHazard ratioOncologyInternal medicineBreast cancerHormone receptorGynecologyCancerConfidence intervalMetastatic breast cancerEstrogen receptor

Abstract

fetched live from OpenAlex

Aim: Palbociclib (PAL), ribociclib (RIB) and abemaciclib (ABM), in combination with fulvestrant (FUL), are approved for the treatment of hormone receptor-positive, HER2-negative advanced breast cancer. This study aims to determine relative efficacy of PAL+FUL versus RIB+FUL and ABM+FUL using matching-adjusted indirect treatment comparisons. Patients & methods: Anchored matching-adjusted indirect treatment comparisons were conducted using individual patient data from PALOMA-3 and published summary-level data from MONARCH 2 and MONALEESA-3. The primary outcome was overall survival (OS). Results: OS was similar for PAL+FUL versus ABM+FUL (hazard ratio: 0.87; 95% CI: 0.54–1.40) and RIB+FUL (hazard ratio: 0.89; 95% CI: 0.48–1.63). Conclusion: Adjusting for cross-trial differences suggests similar OS between treatments, underscoring the importance of accounting for these differences when indirectly comparing treatments.

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)0.014
metaresearch head score (Gemma)0.025
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.098
GPT teacher head0.467
Teacher spread0.368 · 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
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

Citations17
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Comparative Effectiveness ResearchSame topicAdvanced Breast Cancer TherapiesFrench-language works237,207