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Record W2131685192 · doi:10.1177/0284185113506135

Reliability of on-call radiology residents’ interpretation of 64-slice CT pulmonary angiography for the detection of pulmonary embolism

2013· article· en· W2131685192 on OpenAlexaff
Rohit Joshi, Ke Wu, Jatin Kaicker, Hema Choudur

Bibliographic record

VenueActa Radiologica · 2013
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsHamilton Health SciencesVictoria HospitalMcMaster UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsMedicinePulmonary embolismRadiologyAngiographyPulmonary angiographySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Computed tomography (CT) angiography for pulmonary embolism (PE) is the present standard for diagnosing PE. In many teaching hospitals, radiology residents are the first to review the case and to make an initial interpretation of the images. Accurate diagnosis of PE is crucial, especially in the emergency care setting. PURPOSE: To evaluate the discrepancies between resident and staff interpretations of 64-slice CT angiogram for PE. MATERIAL AND METHODS: Discrepancies between the preliminary reports by the on-call radiology resident were compared to the final report by the staff radiologist in 215 consecutive cases of 64-slice CT angiogram performed for PE, from May 2005 to March 2008. RESULTS: Discrepancies were noted in 25 of the 215 studies (11.6%). These residents' discrepancies consisted of three false-positive, four false-negative, and 18 equivocal cases. There was a decrease in the discrepancy rate from the second year to the fifth year of training by approximately 60%. CONCLUSION: The rate of discrepancy fell steeply between the second and fifth year of the residents training from 18.5% to 6.9%. Our study suggests that it is reasonable to have on-call radiology residents perform the preliminary interpretations of 64-slice CT for PE studies.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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