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Record W2888398400 · doi:10.1002/mp.12878

Improving patient outcomes and radiotherapy systems: A pan‐Canadian approach to patient‐reported outcome use

2018· article· en· W2888398400 on OpenAlexafffundabout
Amanda Caissie, Erika Brown, Rob Olson, Lisa Barbera, Carol‐Anne Davis, Michael Brundage, Michael Milosevic

Bibliographic record

VenueMedical Physics · 2018
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsPrincess Margaret Cancer CentreSunnybrook Health Science CentrePositive Living NorthQueen's UniversityUniversity of Northern British ColumbiaUniversity of British ColumbiaHealth Sciences CentreSaint John Regional HospitalUniversity of TorontoDalhousie University
FundersPartenariat Canadien Contre Le Cancer
KeywordsGeneral partnershipRadiation oncologyRadiation therapyMedicineMedical physicistMedical physicsHealth careProfessional associationFamily medicineBusinessPolitical scienceRadiologyPublic relations

Abstract

fetched live from OpenAlex

Standardized collection and use of clinical patient-reported outcomes (PRO) have potential to benefit the care of individual patients and improve radiotherapy system performance. Its centralized health-care system makes Canada a prime candidate to take a leader and collaborator role in international endeavors to promote expansion of patient-reported outcome collection and use in radiotherapy. The current review discusses the development of a pan-Canadian approach to PRO use, through a quality improvement initiative led by the Canadian Partnership for Quality Radiotherapy (CPQR), a unique partnership of Canadian radiotherapy professional organizations (Canadian Association of Radiation Oncology-CARO, Canadian Organization of Medical Physicists-COMP, and the Canadian Association of Medical Radiation Technologists-CAMRT).

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 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)0.055
metaresearch head score (Gemma)0.082
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.595

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.082
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.021
Science and technology studies0.0040.003
Scholarly communication0.0080.003
Open science0.0040.007
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.285
Teacher spread0.246 · 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 designNot applicable
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

Citations10
Published2018
Admission routes3
Has abstractyes

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