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Record W2902634598 · doi:10.4236/ojrad.2018.84031

Rate of Inappropriate Imaging Utilization by the Emergency Department in Community Hospitals

2018· article· en· W2902634598 on OpenAlexaffabout
Sébastien Robert, Murray Asch, Larry Nijmeh

Bibliographic record

VenueOpen Journal of Radiology · 2018
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsLakeridge HealthUniversity of Ottawa
Fundersnot available
KeywordsMedicineEmergency departmentAppropriateness criteriaMedical imagingClinical judgementMedical recordAppropriate Use CriteriaEmergency medicineRadiology

Abstract

fetched live from OpenAlex

Objective: To retrospectively analyse the use of imaging studies in the Emergency Department of community hospitals using evidence based guidelines and clinical judgement. Methods: Medical records of 661 patients who visited the Emergency Department (ED) in 2015 and underwent imaging studies were reviewed. The Canadian Association of Radiologists, American College of Radiologists and Choosing Wisely Canada guidelines were used to determine the appropriateness of imaging studies. The use of prior patient imaging, the rate at which studies were repeated and the respective impacts on patient management of the imaging studies were also examined. Results: Of the 1056 imaging studies reviewed, 228 (22%) were found to be clinical situations where no imaging study was indicated while 168 (16%) were considered a suboptimal choice of imaging study or modality. When no study was recommended, a positive impact on the diagnosis was noted in 105 (46%) cases and on patient management 83 (36%) times. Notably, 219 (21%) patients had a relevant examination performed in the last 30 days, and 147 (14%) reports noted that the results of the prior study also concurred with the imaging study evaluated. Conclusion: In this study, 228 (22%) radiographs and CT studies, excluding MVC related imaging and extremity imaging, were not indicated based on appropriateness criteria and consequently had a limited impact on patient management. This supports the need for increased clinical decision support for ED physicians, regional health information exchanges and consideration of Computerized Physician Order Entry in the ED with embedded appropriateness criteria at the point of ordering.

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)0.002
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 score0.074
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.063
GPT teacher head0.399
Teacher spread0.336 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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