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Record W3184609309 · doi:10.1016/j.adro.2021.100754

A Framework for Assuring the Safety, Training, Evaluation, and Wellness of Radiation Oncology Residents During the COVID-19 Pandemic (ASTEROiD-COVID19)

2021· review· en· W3184609309 on OpenAlexaff
Christopher D. Goodman, Rohann Correa, Andrew Arifin, Robert Dinniwell, Joanna Laba, Timothy K. Nguyen

Bibliographic record

VenueAdvances in Radiation Oncology · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsPandemicRadiation oncologyCoronavirus disease 2019 (COVID-19)MedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakAdaptation (eye)Personal protective equipmentMedical educationMedical emergencyMedical physicsRadiation therapyInfectious disease (medical specialty)DiseasePathologyInternal medicinePsychology

Abstract

fetched live from OpenAlex

As the coronavirus disease 2019 (COVID-19) pandemic continues to disrupt nearly all facets of daily life, residency programs must ensure the safety and wellness of their residents while maintaining a commitment to their training and advancement. In addition to standard clinical training, radiation oncology residency programs integrate highly specialized elements specific to the delivery of radiation therapy. Few publications have addressed the significant effects of the pandemic on medical training and even fewer have addressed concerns specific to radiation oncology. We report our experience developing a resident-led adaptation of our training program in response to the COVID-19 pandemic with the aim of assisting other programs to meet this challenge.

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)0.006
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.208
GPT teacher head0.557
Teacher spread0.349 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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