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Record W2789828475 · doi:10.22454/fammed.2018.877757

Skills, Practice Patterns, and Knowledge of Canadian Family Physician Endoscopists

2018· article· en· W2789828475 on OpenAlexafffundabout
Michael R. Kolber, Shelley Ross

Bibliographic record

VenueFamily Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMedicineEndoscopyContinuing medical educationSigmoidoscopyColorectal cancerFamily medicineColonoscopyContinuing educationGeneral surgerySurgeryInternal medicineMedical educationCancer

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: In Canada, few family physicians (FPs) perform endoscopy. Conflicting evidence exists on the quality of endoscopy performed by Canadian FPs, which may be explained by differing skillsets of these endoscopists. The objective of this study was to perform the first exploration of the practice, skills, and knowledge of Canadian FP endoscopists. METHODS: A cross-sectional survey, including direct knowledge test, was used. RESULTS: Twenty Canadian FP endoscopists completed the survey. Ninety-five percent practice outside urban centres, all perform gastroscopies, and 85% perform colonoscopies and polypectomies. These endoscopists are performing about 32 procedures per month. They are using split bowel preparations, performing rectal retroflexion, and tattooing advanced lesions, all characteristics of a quality endoscopist or program. Self-identified knowledge gaps identified included caring for patients with inflammatory bowel disease and staging rectal cancer. Direct testing found gaps in describing Barrett's esophagitis and managing anticoagulated patients who require endoscopy. CONCLUSIONS: Canadian FP endoscopists appear to be providing quality endoscopic exams and should be supported and encouraged to continue to provide care of rural Canadian patients with gastrointestinal concerns. Future training and continuing education events aimed at this group of endoscopists should target identified knowledge gaps.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.027
GPT teacher head0.312
Teacher spread0.285 · 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 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

Citations0
Published2018
Admission routes3
Has abstractyes

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