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Record W4309346519 · doi:10.1016/j.adro.2022.101097

American Society for Radiation Oncology's Advances in Radiation Oncology in 2022

2022· editorial· en· W4309346519 on OpenAlexaff
Robert C. Miller, C. Jillian Tsai

Bibliographic record

VenueAdvances in Radiation Oncology · 2022
Typeeditorial
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsRadiation oncologyMedicinePublishingClinical OncologyMedical physicsPublicationOncologyInternal medicineRadiation therapyCancerPolitical science

Abstract

fetched live from OpenAlex

This fall marks the American Society for Radiation Oncology's (ASTRO) Advances in Radiation Oncology’s seventh year of publishing. The past year has been our most successful to date with our CiteScore increasing to 4.3 (Fig. 1), placing Advances in Radiation Oncology in the top 3 of comparable journals. A conventional impact factor is expected to be granted in 2023. ASTRO created Advances in Radiation Oncology in 2015 in response to the increasing demand for outlets to publish quality radiation oncology–related research and limitations in the number of manuscripts that could be published in conventional paper journals. As a gold open-access journal, permanently freely available, Advances in Radiation Oncology is available on a global basis to all health care professionals and scientists, as well as our patient population and their caregivers. Figure 2 demonstrates Advances in Radiation Oncology’s complementary relationship with the Red Journal and Practical Radiation Oncology. Figure 3 highlights our current most-downloaded articles.Figure 3Top downloads in Advances in Radiation Oncology in August 2022.View Large Image Figure ViewerDownload Hi-res image Download (PPT) We are particularly interested this year in publishing more scholarly critical reviews. Our current call for articles includes those relating to the use of radiopharmaceuticals and cybersecurity issues in radiation oncology. In recent years, the disruption in care delivery from issues (eg, COVID-19; cyberattacks; and ongoing humanitarian crises in Ukraine [Fig. 4], Syria, and elsewhere) have been the focus of many of our most-read articles. Two articles are included in this collection. A report from Istanbul by Uğurluer et al1Uğurluer G Özyar E Corapcioglu F Miller RC Psychosocial impact of the war in Ukraine on pediatric cancer patients and their families receiving oncological care outside their country at the onset of hostilities.Adv Radiat Oncol. 2002; 7100957Google Scholar describes the psychosocial issues facing Ukrainian families whose children were receiving cancer care in Turkey at the onset of the war in Ukraine. Flavin et al2Flavin A O'Toole E Murphy L et al.A national cyberattack affecting radiation therapy: The Irish experience.Adv Radiat Oncol. 2022; 7100914Google Scholar report on the national effect on radiation therapy delivery after a cyberattack on the public health services of the Republic of Ireland. Additional review articles included in this issue are a review of the management of implantable devices by Chan et al3Chan MF Young C Gelblum D et al.A review and analysis of managing commonly seen implanted devices for patients undergoing radiation therapy.Adv Radiat Oncol. 2021; 6100732Google Scholar and on the Abscopal effect by Hatten et al.4Hatten Jr, SJ Lehrer EJ Liao J et al.A patient-level data meta-analysis of the Abscopal effect.Adv Radiat Oncol. 2022; 7100909PubMed Google Scholar We welcome inquiries regarding proposed review articles and encourage potential authors to contact the editor in chief directly at [email protected] to discuss their proposal. Our most downloaded article at this time is a paper on breast cancer reirradiation by Fattahi et al.5Fattahi S Ahmed SK Park SS et al.Reirradiation for locoregional recurrent breast cancer.Adv Radiat Oncol. 2020; 6100640PubMed Google Scholar Social media–focused articles and analyses of professional issues related to resident training and the job market are also highly read. Included in this issue is a report characterizing Twitter influencers in radiation oncology by Valle et al6Valle LF Chu FI Smith M et al.Characterizing Twitter influencers in radiation oncology.Adv Radiat Oncol. 2022; 7100919Google Scholar that is one of our most highly read articles. We thank the Advances in Radiation Oncology editorial board, our reviewers, and the ASTRO community for all their time and diligence dedicated to our past 7 years of success. We look forward to continued growth. Advances in Radiation Oncology is committed to diversity and inclusion on the editorial team.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0030.004
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 head0.009
GPT teacher head0.424
Teacher spread0.416 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2022
Admission routes1
Has abstractyes

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