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Record W2782745614 · doi:10.1177/8756479317750108

Feasibility of an Audit System for Canadian Sonographers in Generalist Ultrasound

2018· article· en· W2782745614 on OpenAlexaffabout
Robert Dima, Calin Vasile, Vinicius Tieppo Francio

Bibliographic record

VenueJournal of diagnostic medical sonography · 2018
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsHeadwaters Health Care CentreMcMaster University
Fundersnot available
KeywordsSonographerMedicineAuditMedical physicsUltrasoundRadiologyAccounting

Abstract

fetched live from OpenAlex

The purpose of this research was to employ the audit method to measure performance and identify targets of change, setting a template for future large-scale investigations that may inform decisions involving sonographer role expansion in Canada. The authors conducted an audit of 433 sonographic examinations performed in the ultrasound department of a Canadian hospital. Sonographer reports were contrasted with radiologist final reports, and a degree of agreement (DoA) 1 to 4 was assigned to each exam package. In total, 322 of 429 (75%) exam packages were ranked as DoA 1 (complete agreement between sonographer and radiologist), 86 of 429 (20%) were ranked as DoA 2, 16 of 429 (4%) were ranked as DoA 3, and 5 of 429 (1%) were ranked as DoA 4 (significant discrepancy between sonographer and radiologist). The results revealed a 75% agreement between sonographer and radiologist on imaging findings as they are recorded in technical impression sheets and reports. Discrepancies are usually minor and involve the omission of incidental findings by the radiologist.

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)0.083
metaresearch head score (Gemma)0.160
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.966
Threshold uncertainty score0.696

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.160
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0050.001
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.344
Teacher spread0.311 · 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 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

Citations6
Published2018
Admission routes2
Has abstractyes

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