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Record W2484915095 · doi:10.1016/j.breast.2016.07.006

Radiotherapy for breast cancer: The predictable consequences of an unmet need

2016· article· en· W2484915095 on OpenAlexaff
Danielle Rodin, Felícia Marie Knaul, Tracey Y M Lui, Mary Gospodarowicz

Bibliographic record

VenueThe Breast · 2016
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineRadiation therapyBreast cancerCommissionCancerDiseaseFamily medicineIntensive care medicineSurgeryInternal medicineFinance

Abstract

fetched live from OpenAlex

Radiotherapy has had a transformative impact on the treatment of breast cancer, but is unavailable to the majority of breast cancer patients in low- and middle-income countries. In these settings, where many women present with advanced disease at an age when they are often the primary caregiver for their families, the lack of access to radiotherapy is particularly devastating. Until recently, this disparity has been largely neglected in the medical literature and it had been difficult to convince governments, industry, and policymakers of the importance of investing in radiotherapy, as well as broader cancer control strategies, in low-resource settings. The Lancet Radiotherapy Commission report published in 2015 challenged many assumptions about the affordability of radiotherapy treatment. Data from the Commission is presented here to support radiotherapy investment for breast cancer and discuss how the morbidity and premature mortality among adult women caused by breast cancer has a huge detrimental effect on both the health sector and the economy.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.624

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.345
Teacher spread0.331 · 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
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

Citations35
Published2016
Admission routes1
Has abstractyes

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