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Record W273613425 · doi:10.1155/2011/608283

Development and Implementation of a Comprehensive Quality Assurance Program at a Community Endoscopy Facility

2011· article· en· W273613425 on OpenAlexaffvenueabout
Robert J. Hilsden, Alaa Rostom, Catherine Dubé, Darlene Pontifex, S. Elizabeth McGregor, Ronald Bridges

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2011
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsQuality assuranceMedicineColonoscopyQuality managementService (business)Unit (ring theory)Quality (philosophy)Medical physicsColorectal cancerProcess managementCancerBusinessPathologyPsychology

Abstract

fetched live from OpenAlex

Quality assurance (QA) is a process that includes the systematic evaluation of a service, institution of improvements and ongoing evaluation to ensure that effective changes were made. QA is a fundamental component of any organized colorectal cancer screening program. However, it should play an equally important role in opportunistic screening. Establishing the processes and procedures for a comprehensive QA program can be a daunting proposition for an endoscopy unit. The present article describes the steps taken to establish a QA program at the Forzani & MacPhail Colon Cancer Screening Centre (Calgary, Alberta) - a colorectal cancer screening centre and nonhospital endoscopy unit that is dedicated to providing colorectal cancer screening-related colonoscopies. Lessons drawn from the authors' experience may help others develop their own initiatives. The Global Rating Scale, a quality assessment and improvement tool developed for the gastrointestinal endoscopy services of the United Kingdom's National Health Service, was used as the framework to develop the QA program. QA activities include monitoring the patient experience through surveys, creating endoscopist report cards on colonoscopy performance, tracking and evaluating adverse events and monitoring wait times.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.316
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.069
GPT teacher head0.328
Teacher spread0.259 · 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

Citations16
Published2011
Admission routes3
Has abstractyes

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