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Record W4413996556 · doi:10.1016/j.tipsro.2025.100343

Beyond the first course: Re-irradiation practices in Ontario — Insights from a provincial survey

2025· article· en· W4413996556 on OpenAlexaffabout
Brian Liszewski, Timothy P. Hanna, Nareesa Ishmail, Kyle Malkoske, Laura D’Alimonte, Jason Pantarotto, Eric Gutierrez, Julie Kraus, Angelica Ramprashad, Kristin H. Berry

Bibliographic record

VenueTechnical Innovations & Patient Support in Radiation Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsOttawa HospitalRoyal Victoria HospitalCancer Care Ontario
Fundersnot available
KeywordsCourse (navigation)Medical educationMedicineEngineering

Abstract

fetched live from OpenAlex

Re-irradiation is an increasingly important aspect of cancer care, as more patients undergo more complex, multi-course radiation therapy, often across multiple cancer centres. To better understand how re-irradiation is planned and delivered, Ontario Health (Cancer Care Ontario)'s Radiation Treatment Program conducted a provincial review using administrative data and a structured survey of all 15 Regional Cancer Centres (RCC) that provide all radiation therapy to Ontario's 16 million people. The findings offered insight into current practices, including institutional policies, clinical workflows, technical planning methods, and interprofessional collaboration. As the complexity of care continues to grow, there is a clear need to harmonize these elements across institutions to support the safe, effective, and consistent delivery of re-irradiation. These findings are helping inform system-wide efforts to strengthen coordination and improve quality across the RCCs within Ontario.

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)0.002
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: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.362
Teacher spread0.323 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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