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Record W3003124347 · doi:10.21037/tcr.2019.07.18

Stereotactic radiation therapy for breast cancer in the elderly

2020· review· en· W3003124347 on OpenAlexaff
P. Jardel, Emmanuel Kammerer, Hugo Villeneuve, Juliette Thariat

Bibliographic record

VenueTranslational Cancer Research · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean
Fundersnot available
KeywordsMedicineTolerabilityBreast cancerRadiation therapyRadiosurgeryOncologyStereotactic radiotherapyInternal medicineRadiologyCancerAdverse effect

Abstract

fetched live from OpenAlex

Abstract: The management of breast cancer in elderly women is going to be a major public health issue in a near future. The use of hypofractionated stereotactic radiotherapy is expanding but might be a priori not offered to older patients. We addressed the role of stereotactic radiotherapy (SBRT, 1–10 fractions) in elderly patients with breast cancer, in definitive, adjuvant and metastatic settings. Review of the literature. Of six series using SBRT for partial breast or breast boost irradiation and over 20 oligometastatic (brain, lung, liver, bone) SBRT series including patients aged ≥60 years old, no difference was found in term of efficacy (>80%) and toxicity (<5% G3-4) compared to the younger. Hypofractionation is also well adapted to the elderly, due to limited transportation-related fatigue. SBRT studies by age group are lacking. However, hypofractionated SBRT is particularly adapted to older patients with breast cancer, in term of efficacy and tolerability and should be encouraged rather than more morbid treatments whenever possible.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.136
GPT teacher head0.468
Teacher spread0.332 · 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 designNot applicable
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

Citations6
Published2020
Admission routes1
Has abstractyes

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