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Record W4416974763 · doi:10.12809/hkmj2513054

Expert consensus recommendations on the daily clinical use of pembrolizumab for early triple-negative breast cancer

2025· article· en· W4416974763 on OpenAlexaff
Winnie Yeo, Yolanda HY Chan, Roland CY Leung, William Foo, Sara Fung, Carol CH Kwok, Stephanie HY Lau, Alex Leung, Ting Ying Ng, Janice Tsang, Iris L. K. Wong, Chun Chung Yau, M Yuen, Polly SY Cheung

Bibliographic record

VenueHong Kong Medical Journal · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsPembrolizumabBreast cancerPerioperativeNeoadjuvant therapyChemotherapyMastectomyClinical trialMEDLINE

Abstract

fetched live from OpenAlex

Neoadjuvant chemotherapy is a standard treatment for triple-negative breast cancer (TNBC) at an early stage. Given that pathological complete response is strongly associated with long-term clinical and survival benefits, the selection of appropriate treatment before and after surgery could further optimise treatment outcomes. With the emergence of immunotherapy in breast cancer, more combination treatment options are available, such as pembrolizumab, a programmed death receptor 1 inhibitor, which is approved for the perioperative treatment of stage II and III TNBC. However, the implementation of immunotherapy in perioperative settings for TNBC requires further discussion regarding patient selection and the use of different treatments in conjunction with immunotherapy. The Hong Kong Breast Cancer Foundation convened a multidisciplinary consensus panel consisting of surgeons, clinical oncologists, and medical oncologists to initiate this discussion. A modified Delphi panel was conducted, evaluating seven topics and 45 statements covering the workup and perioperative treatment of early-stage TNBC (eTNBC). The consensus statements provide guidance on determining whether a patient with eTNBC is a suitable candidate for neoadjuvant chemotherapy and immunotherapy.

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)0.002
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 score0.810
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.054
GPT teacher head0.380
Teacher spread0.326 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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