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Record W4413007494 · doi:10.3390/curroncol32080441

Efficacy and Safety of Dose-Dense Chemotherapy in Breast Cancer: Real Clinical Data and Literature Review

2025· review· en· W4413007494 on OpenAlexvenueno aff
Keiko Yanagihara, Masato Yoshida, Miki Tamura, Koji Nagata

Bibliographic record

VenueCurrent Oncology · 2025
Typereview
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePegfilgrastimBreast cancerDocetaxelChemotherapyInternal medicineOncologyEpirubicinAdverse effectCyclophosphamideNeutropeniaLeukocytopeniaRegimenFebrile neutropeniaCancer

Abstract

fetched live from OpenAlex

Dose-dense chemotherapy shortens the interval between chemotherapy cycles and has shown improved outcomes in high-risk breast cancer patients. We retrospectively evaluated the efficacy and safety of dose-dense chemotherapy in 80 breast cancer patients treated at our hospital from 2020 to 2024. The regimen included epirubicin and cyclophosphamide followed by paclitaxel or docetaxel, with pegfilgrastim support. The overall treatment completion rate was 82.5%. Of the 80 patients, 55 underwent neoadjuvant chemotherapy, and the pathological complete response rate was significantly higher in triple-negative breast cancer (59.1%) compared to that in luminal-type cancer (9.1%). Common adverse events included anemia, liver dysfunction, myalgia, and peripheral neuropathy. Febrile neutropenia occurred in 8.8% of patients, with some cases linked to pegfilgrastim body pod use, particularly in individuals with low subcutaneous fat. Notably, two patients developed pneumocystis pneumonia, potentially associated with steroid administration. Despite these toxicities, most were manageable and resolved after treatment. Our findings support the efficacy of dose-dense chemotherapy, particularly in triple-negative breast cancer, while highlighting the importance of individualized supportive care and vigilance regarding hematologic and infectious complications.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.877
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.176
GPT teacher head0.547
Teacher spread0.371 · 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 designOther design
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
Published2025
Admission routes1
Has abstractyes

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