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Record W3134530691 · doi:10.4103/eus-d-20-00234

Standard reporting elements for the performance of EUS: Recommendations from the FOCUS working group

2021· article· en· W3134530691 on OpenAlexaffabout
Paul D. James, Suqing Li, Marc Monachese, Misbah Salim, Naveen Arya, AnandV Sahai, Nauzer Forbes, Christopher Teshima, Mohammad Yaghoobi, Yen‐I Chen, Eric Lam

Bibliographic record

VenueEndoscopic Ultrasound · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsMcGill University Health CentreUniversity of British ColumbiaMcMaster University Medical CentreSt. Michael's HospitalUniversity of CalgaryUniversité de MontréalUniversity Health NetworkUniversity of TorontoOakville-Trafalgar Memorial Hospital
Fundersnot available
KeywordsMedicineDocumentationMedical physicsQuality (philosophy)Endoscopic ultrasoundQuality managementRadiologyComputer scienceService (business)

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Quality indicators for the performance of EUS have been developed to monitor and improve service value and patient outcomes. To support the incorporation of these indicators and standardize EUS documentation, we propose standard EUS reporting elements for endosonographers and endoscopy units. METHODS: A comprehensive literature search and review was performed to identify EUS quality indicators and key components of high-quality standardized EUS reporting. Guidance statements regarding standard EUS reporting elements were developed and reviewed at the Forum for Canadian Endoscopic Ultrasound (FOCUS) 2019 Annual Meeting. RESULTS: EUS reporting elements can be divided into preprocedural, intraprocedural, and postprocedural items. Preprocedural components include the type, indication, and urgency of the procedure and patient clinical information and consent. Intraprocedural components include the adequacy and extent of examination, relevant landmarks, lesion characteristics, sampling method, specimen quality, and intraprocedural adverse events. Postprocedural components include a summary and synthesis of relevant findings as well as recommended management and follow-up. CONCLUSIONS: Standardizing reporting elements may help improve the care of patients undergoing EUS procedures. Our review provides a practical guide and compilation of recommended reporting elements to ensure ongoing best practices and quality improvement in EUS.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.153
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0010.000

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 head0.062
GPT teacher head0.368
Teacher spread0.306 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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