MétaCan
Menu
Back to cohort

The cancer imaging program quality framework at Cancer Care Ontario: The first five years.

2014· article· en· W2473214082 on OpenAlexaffabout
Julian Dobranowski, Saul Melamed, Deanna L. Langer, Colleen Bedford

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsCancer Care Ontario
Fundersnot available
KeywordsMedicineMedical physicsCancerQuality (philosophy)Lung cancerFamily medicineRadiologyPathology

Abstract

fetched live from OpenAlex

244 Background: The Cancer Imaging Program (CIP) at Cancer Care Ontario was established in 2009 to improve the quality of cancer imaging in Ontario. Methods: After initial selection of a Provincial clinical lead in 2009, fourteen regional clinical leads were selected to represent all geographical regions of the province. Through a stakeholder survey and a priority setting process the following four high-level areas of priority emerged to support quality improvement of cancer imaging: (1) Developing and Fostering an Imaging Community of Practice, (2) Imaging Appropriateness, (3) Timely Access to Imaging, and (4) Standardized/Synoptic Reporting. Results: (1) An Imaging Community of Practice was established with the regional clinical leads, who participate in monthly meetings to build and strengthen inter-regional relationships and share information on regional activities and priorities; (2) Best practice standards for imaging in lung and colorectal cancer have been developed by consolidating and endorsing national and international guidelines. New imaging guidelines are being developed by the Program in Evidence-Based Care. Evidence-based recommendations being developed for focal tumour ablation procedures; (3) Three Interventional Radiology procedures (CT-guided lung biopsies, peripherally inserted central catheters and portacaths) have been selected for an ongoing wait time collection that captures monthly point-in-time data. The data has initiated discussions on appropriate benchmarks and identification of factors that may contribute wait times; and (4) Synoptic Radiology Reporting enables the collection of uniform and complete data to improve the information available to referring clinicians for diagnosis and treatment planning. Work is underway in the development of: implementation roadmap, evidence-based clinical checklists, infrastructure to store and share synoptic reports, and international standards for synoptic radiology reporting. Conclusions: The establishment of the CIP as a clinical program under a provincial cancer agency has enabled the development of an Imaging Community of Practice and allowed for work on provincial-wide initiatives that enable quality improvement of cancer imaging.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.131
GPT teacher head0.566
Teacher spread0.435 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicRadiology practices and educationFrench-language works237,207