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Record W4293000937 · doi:10.54097/hset.v8i.1219

The Combination Therapy in Breast Cancer Treatment

2022· article· en· W4293000937 on OpenAlex
Qianbing Liu, Yuxin Mei, Weiyi Zhang, Yunkai Zhang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBreast cancerMedicineOncologyCombination therapyInternal medicineMetastatic breast cancerChemotherapyCancerDrug resistanceDrugPharmacology

Abstract

fetched live from OpenAlex

Breast cancer (BC) is the cancer that most commonly diagnosed worldwide, which result in the cause of cancer-related deaths. The majority of BC diagnoses were HR+ and HER2- (71%) and HER2+BC accounts for 10-20% of all breast tumors. There is no magic drug for the treatment of breast cancer at present. Endocrine therapy is the preferable treatment for HR+/HER2- metastatic breast cancer. However, long-term use may produce certain drug resistance. Tucatinib, as a HER2 inhibitor, can be combined with chemotherapy to treat HER2+BC.Combination therapy can offers patients the opportunity to derive the maximum benefit from treatment, at the same time, it can minimize or eliminate relapse, drug resistance and toxic effects and thus the BC patients can have a good quality of life. This paper discussed the combination therapy of endocrine therapy or tucatinib with other drugs and compared their advantages and disadvantages in breast cancer therapy, providing better choice for clinical treatment of BC.

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.

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)0.000
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 score0.502
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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