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Record W4388908737 · doi:10.1177/23969873231214218

Diagnostic accuracy of large and medium vessel occlusions in acute stroke imaging by neurology residents and stroke fellows: A comparison of CT angiography alone and CT angiography with CT perfusion

2023· article· en· W4388908737 on OpenAlexaff
Fawaz F Alotaibi, Asma Alshahrani, Gamal Mohamed, Mohamed A AlShamrani, Hussain Bin Amir, Ali Y. Alsaeed, Alaa Heji, Suwaidi Alghanmi, Mohammed Alqurishi, Azhar Alanazi, Hamad Aldraye, Muhannad Asiri, Mohammed S. Alqahtani, Abdulrahman Alreshaid, Ammar Alkawi, Adel Alhazzani, Mohamed Alzawahmah, Riyadh Alokaili, Ashfaq Shuaib, Fahad Al-Ajlan

Bibliographic record

VenueEuropean Stroke Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineNeurologyStroke (engine)RadiologyAngiographyAcute strokeNeuroradiologyNeuroimagingComputed tomography angiographyPerfusion scanningCerebral angiographyNeuroradiologistPerfusionNuclear medicineInternal medicineMagnetic resonance imagingEmergency departmentPsychiatry

Abstract

fetched live from OpenAlex

Abstract Introduction: Neurology senior residents and stroke fellows are first to clinically assess and interpret imaging studies of patients presenting to the emergency department with acute stroke. The aim of this study was to compare the diagnostic accuracy of brain CT angiography (CTA) with and without CT perfusion (CTP) between neurology senior residents and stroke fellows. Methods: In this neuroimaging study, nine practitioners (four senior neurology residents (SNRs) and five stroke fellows (SFs)) clinically assessed and interpreted the imaging data of 50 cases (15 normal images, 21 large vessel occlusions (LVOs) and 14 medium vessel occlusions (MeVOs) in two sessions, 1 week apart in comparison to final diagnosis of experienced neuroradiologist and experienced stroke neurologist consensus. Interrater agreement of CTA alone and CTA with CTP was quantified using kappa statistics, sensitivity, specificity and overall accuracy. Results: Overall, arterial occlusions were correctly identified in 221/315 (70.1%) with CTA alone and in 266/315 (84.4%) with CTA and CTP (p < 0.001). The sensitivity of overall arterial occlusions detection with CTA alone was 94.2% (95% CI: 90.8%–96.6%) while with addition of CTP was 98% (95% CI: 95.6%–99.3%), The specificity of CTA alone was 74.7% (95% CI: 67.2%–81.3%) which increased with CTP to 84.4% (95% CI: 77.7%–89.8%). The likelihood of correct identification with CTA alone was 156/189 (82.54%) for LVOs and 65/126 (51.59%) for MeVOs. This increased to 169/189 (89.42%; p = 0.054) for LVOs and 97/126 (76.98%; p < 0.001) for MeVOs when the CTA images with CTP were viewed. There was good overall interrater agreement between readers when using CTA alone (k 0.71, 95% CI, 0.62–0.80) and almost perfect (k 0.85, 95% CI, 0.76–0.94) when CTP was added to the image for interpretation. CTA and CTP had a significantly lower median interquartile range (IQR) interpretation time than CTA alone (114 [IQR, 103–120] s vs 156 [IQR, 133–160] s, p < 0.001). Discussion: In cerebral arterial occlusions, the rate of LVO and MeVOs detections increases when adding CTP to CTA. The accuracy and time for diagnosing arterial occlusion can be significantly improved if CTP is added to CTA. As MeVOs are commonly missed by front-line neurology senior residents or stroke fellows, cases with significant deficits and no apparent arterial occlusions need to be reviewed with neuroradiological expertise.

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)0.011
metaresearch head score (Gemma)0.039
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 score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.008
GPT teacher head0.269
Teacher spread0.261 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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