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Record W3138420864 · doi:10.1016/j.tipsro.2021.01.003

A Canadian experience of palliative advanced practice radiation therapy TIPS: Training, implementation, practice and sustainability

2021· article· en· W3138420864 on OpenAlexafffundabout
Natalie Rozanec, Carrie Lavergne, Nicole Harnett

Bibliographic record

VenueTechnical Innovations & Patient Support in Radiation Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsPrincess Margaret Cancer CentreLakeridge HealthRegional Municipality of DurhamUniversity of TorontoSouthlake Regional Health Center
FundersAssociation Canadienne des Technologues en Radiation MédicaleCancer Care Ontario
KeywordsSustainabilityTraining (meteorology)MedicineRadiation oncologyRadiation therapyClinical PracticePalliative careMedical educationMedical physicsSurgeryNursingGeographyEcology

Abstract

fetched live from OpenAlex

The concept of the Advanced Practice Radiation Therapist (APRT) was created in 2004, in response to pressures on the radiation treatment sector in Ontario. This led to development, piloting and integration of the Clinical Specialist Radiation Therapist (CSRT) into Ontario's cancer care framework. A national certification process, competency profile and protected title of APRT(T) were established in 2017, under the Canadian Association of Medical Radiation Technologists (CAMRT), in collaboration with Cancer Care Ontario/Ontario Health. This report describes the approach to development, validation and measuring impact of the CSRT role in Ontario, specifically in palliative care (pCSRT). It also presents information to assist jurisdictions interested in developing a pCSRT position, describing competency development, assessment, and assumption of practice, and providing some keys to success. This is foundational for consistent expansion of the pCSRT role to other regions to continue to increase system capacity while improving the quality of cancer care.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.430
Teacher spread0.393 · 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 designOther design
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

Citations23
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueTechnical Innovations & Patient Support in Radiation OncologySame topicManagement of metastatic bone diseaseFrench-language works237,207