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Record W2161281057 · doi:10.1148/rg.327105701

Informatics in Radiology: Web-based Preliminary Reporting System for Radiology Residents with PACS Integration

2012· article· en· W2161281057 on OpenAlexaff
Timothy O’Connell, Debra Chang

Bibliographic record

VenueRadiographics · 2012
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWorkflowDICOMMedicineInformaticsHealth informaticsPicture archiving and communication systemWeb applicationWorld Wide WebProcess (computing)Computer scienceRadiologyPathologyDatabase

Abstract

fetched live from OpenAlex

While on call, radiology residents review imaging studies and issue preliminary reports to referring clinicians. In the absence of an integrated reporting system at the training sites of the authors' institution, residents were typing and faxing preliminary reports. To partially automate the on-call resident workflow, a Web-based system for resident reporting was developed by using the free open-source xAMP Web application framework and an open-source DICOM (Digital Imaging and Communications in Medicine) software toolkit, with the goals of reducing errors and lowering barriers to education. This reporting system integrates with the picture archiving and communication system to display a worklist of studies. Patient data are automatically entered in the preliminary report to prevent identification errors and simplify the report creation process. When the final report for a resident's on-call study is available, the reporting system queries the report broker for the final report, and then displays the preliminary report side by side with the final report, thus simplifying the review process and encouraging review of all of the resident's reports. The xAMP Web application framework should be considered for development of radiology department informatics projects owing to its zero cost, minimal hardware requirements, ease of programming, and large support community.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.036
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.035
GPT teacher head0.321
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 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

Citations9
Published2012
Admission routes1
Has abstractyes

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