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Record W3013008307 · doi:10.1016/j.adro.2020.03.011

Adapting Radiation Therapy Treatments for Patients with Breast Cancer During the COVID-19 Pandemic: Hypo-Fractionation and Accelerated Partial Breast Irradiation to Address World Health Organization Recommendations

2020· article· en· W3013008307 on OpenAlexaff
Abdulla Al‐Rashdan, Michael Roumeliotis, Sarah Quirk, Petra Grendarova, Tien Phan, Jeffery Cao, Natalie Logie, Wendy Smith, Lisa Barbera

Bibliographic record

VenueAdvances in Radiation Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsPrairie Bible InstituteUniversity of Calgary
FundersGenentech
KeywordsMedicineCoronavirus disease 2019 (COVID-19)PandemicBreast cancerRadiation therapy2019-20 coronavirus outbreakCancerOncologyInternal medicineVirologyDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

On March 11, 2020, the World Health Organization declared the COVID-19 outbreak a global pandemic. Public health officials have urged communities to minimize transmission by changing their habits, including posttravel self-isolation, increased hygiene vigilance, remote working, and social distancing.1 To accommodate these measures, radiation therapy departments are adapting by limiting on-site staff, patient visits, and patient-to-staff interactions, reconciling the goals of minimizing exposure for both patients and health care providers while maintaining quality cancer care.

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)0.006
metaresearch head score (Gemma)0.019
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: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.034
GPT teacher head0.364
Teacher spread0.330 · 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
GenreMethods

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

Citations49
Published2020
Admission routes1
Has abstractyes

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