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Record W4255186579 · doi:10.1016/j.ijrobp.2021.05.164

Impact of the COVID-19 Pandemic on Postgraduate Training in Radiation Oncology

2021· article· en· W4255186579 on OpenAlexaff
Meredith Giuliani, Diana Samoil, Ankit Agarwal, Jennifer Croke, Eleni Giannopoulos, Daniel W. Golden, Ariel E. Hirsch, Rachel Jimenez, Nauman Malik, Janet Papadakos, Naa Kwarley Quartey, Che Hsuan David Wu, Paris‐Ann Ingledew

Bibliographic record

VenueInternational Journal of Radiation Oncology*Biology*Physics · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryCancer Care OntarioPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Radiation oncologyCoping (psychology)PandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyMedicineFamily medicine2019-20 coronavirus outbreakRadiation therapyInternal medicineClinical psychologyPathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0310.003

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 head0.116
GPT teacher head0.470
Teacher spread0.354 · 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 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

Citations0
Published2021
Admission routes1
Has abstractno

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