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Record W4392594797 · doi:10.1186/s40959-024-00215-3

Late cardiotoxicity related to HER2-targeted cancer therapy

2024· letter· en· W4392594797 on OpenAlexaff
Isabelle Senechal, María Sol Andrés, Jieli Tong, Ylenia Perone, Sivatharshini Ramalingam, Muhummad Sohaib Nazir, Stuart D. Rosen, Nicholas C. Turner, Alistair Ring, Alexander R. Lyon

Bibliographic record

VenueCardio-Oncology · 2024
Typeletter
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsCentre hospitalier universitaire de Québec
FundersFondation Leducq
KeywordsMedicineCardiotoxicityEjection fractionCancerInternal medicineCohortIncidence (geometry)Heart failurePopulationTrastuzumabRetrospective cohort studyOncologyBreast cancerChemotherapy

Abstract

fetched live from OpenAlex

Long-term anti-HER2 therapy in metastatic HER2 + cancers is increasing, but data about the incidence and risk factors for developing late Cancer therapy-related cardiac dysfunction (CTRCD) are missing. We conducted a single-centre, retrospective analysis of a cohort of late anti-HER2 related cardiac dysfunction referred to our Cardio-Oncology service. We include seventeen patients with metastatic disease who developed CTRCD after at least five years of continuous anti-HER2 therapy. Events occurred after a median time of 6.5 years (IQR 5.3-9.0) on anti-HER2 therapy. The lowest (median) LVEF and GLS were 49% (IQR 45-55) and - 15.4% (IQR - 14.9 - -16.3) respectively. All our patients continued or restarted, after a brief interruption, their anti-HER2 therapy. Most (16/17) were started on heart failure medical therapy and normalized their left ventricular ejection fraction at a follow-up. Our study has demonstrated that CTRCD can occur after many years of stability on anti-HER2 therapy and reinforces the importance of continuing cardiovascular surveillance in this population.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.328
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations6
Published2024
Admission routes1
Has abstractyes

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