MétaCan
Menu
Back to cohort
Record W3024103642 · doi:10.1016/j.adro.2020.04.017

In Regard to Yerramilli et al’s “Palliative Radiotherapy for Oncologic Emergencies in the Setting of COVID-19: Approaches to Balancing Risks and Benefits”

2020· article· en· W3024103642 on OpenAlexaff
Ezra Hahn, Jonathan Livergant, Barbara‐Ann Millar, Jolie Ringash, Rebecca Wong, Laura A. Dawson, Padraig Warde, Bernard Cummings

Bibliographic record

VenueAdvances in Radiation Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of British ColumbiaPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineRadiation therapyCoronavirus disease 2019 (COVID-19)ScopusPalliative careMEDLINEInternal medicineDisease

Abstract

fetched live from OpenAlex

This timely paper provides guidance on short course palliative radiation therapy (RT) for common indications (eg, brain metastases, cord compression, tumor bleeding, airway obstruction, and bone metastases) during the coronavirus disease 2019 (COVID-19) pandemic.1 The increased risk of patients with cancer contracting COVID-19 infection and their higher risk of morbidity and mortality are strong motivators for using the shortest, most effective palliative RT regimens.1,2

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.453
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.299
GPT teacher head0.492
Teacher spread0.193 · 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 designObservational
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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Radiation OncologySame topicCOVID-19 and healthcare impactsFrench-language works237,207