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Record W4280504068 · doi:10.3390/curroncol29050296

Striving to Fill in Gaps between Clinical Practice and Standards: The Evolution of a Pan-Canadian Approach to Patient-Reported Outcomes Use

2022· review· en· W4280504068 on OpenAlexafffundvenueabout
Amanda Caissie, Robert Olson, Lisa Barbera, Jennifer O’Donnell, Carol‐Anne Davis, Jennifer Croke, Louise Bird, J. Kildea, Erika Brown, Michael Brundage, Michael Milosevic

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsCanadian Association of PhysicistsCBC (Canada)Princess Margaret Cancer CentreMcGill UniversityUniversity of CalgaryUniversity of Northern British ColumbiaQueen's UniversityUniversity of British ColumbiaUniversity of TorontoDalhousie University
FundersPartenariat Canadien Contre Le CancerF. Hoffmann-La RochePfizer
KeywordsMedicineGeneral partnershipChampionMultidisciplinary approachGuidelineClinical PracticeQuality (philosophy)Quality managementFamily medicineMedical educationPolitical scienceManagementPathology

Abstract

fetched live from OpenAlex

Despite the known importance and necessity of the standardized collection and use of patient-reported outcomes (PROs), there remain challenges to successful clinical implementation. Facilitated through a quality improvement initiative spearheaded by the Canadian Partnership for Quality Radiotherapy (CPQR), and now guided by the Canadian Association of Radiation Oncology (CARO)'s Quality and Standards Committee, patient representatives and early-adopter radiation treatment programs continue to champion the expansion of PROs initiatives across the country. The current review discusses the evolution of a pan-Canadian approach to PROs use, striving to fill in gaps between clinical practice and guideline recommendations through multi-centre and multidisciplinary collaboration.

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)0.031
metaresearch head score (Gemma)0.041
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: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.788

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.010
Science and technology studies0.0010.003
Scholarly communication0.0060.003
Open science0.0030.003
Research integrity0.0020.005
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 head0.255
GPT teacher head0.560
Teacher spread0.304 · 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
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

Citations8
Published2022
Admission routes4
Has abstractyes

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