Abstract 2813: Acute Tele-Stroke-Service by Stroke Neurologists: Reliability of Clinically Relevant CT-Findings
Bibliographic record
Abstract
Background: The Stroke East Saxony Network (SOS-NET) provides tele-consultations for acute stroke patients presenting to local hospitals in eastern Saxony, Germany. Experienced stroke neurologists perform tele-consultations 24/7. They take a clinical history, perform a video examination and a standardized evaluation of acute cerebral CT including Alberta Stroke Program Early CT Score (ASPECTS) before suggesting further management. We assessed the diagnostic accuracy and clinical impact of CT evaluation by stroke neurologists in this acute tele-stroke-service. Methods: To assess the diagnostic accuracy of stroke neurologists in CT-interpretation, two experienced neuroradiologists re-evaluated all CT scans of tele-consultations in 2009 blinded to clinical findings. We defined discrepant findings as deviations in the ASPECTS >1 or if differential diagnoses like tumor or intracranial hemorrhage (ICH) were not detected by the stroke neurologists. To assess the clinical impact, all discrepant results were subsequently discussed and re-evaluated of all involved neurologists and neuroradiologists regarding its possible influence on therapeutic decisions and on patients’ outcome. Results: In 2009, we performed 583 tele-consultations (353 ischemic strokes, 119 primary ICH and 111 stroke mimics). In 102 patients with ischemic stroke (29%) thrombolysis was performed with eight bleeding complications (7.8%). The neuroradiologists detected discrepant findings in 44 CT scans (7.5%), however corrected, their image interpretation if unblinded to the clinical information in four patients. We regarded the diagnostic inaccuracy by neurologist as clinically relevant in nine patients (1.5%). In one patient, the stroke neurologist missed a small chronic subdural hematoma. The patient did not receive thrombolysis because of mild clinical findings. Consequently, the false diagnosis did not affect treatment and clinical outcome in this patient. Furthermore, stroke neurologists missed extensive early ischemic changes in the middle cerebral artery territory in eight patients and recommended thrombolytic therapy. We observed symptomatic ICH in five of these patients (62.5%; 0.9% of the total cohort). Conclusion: Trained stroke neurologists had a high diagnostic accuracy in CT interpretation in acute tele-stroke-service, compared to experienced neuroradiologists. Clinically relevant misinterpretations of the CT scans were rare (1.5%). However, regarding the potential risk of thrombolytics, stroke neurologists should continuously be trained in interpreting CT scans of acute stroke patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".