Abstract TP60: Effect of Clinical History in Radiologist Interpretation of Computed Tomography for Acute Stroke
Bibliographic record
Abstract
Introduction: Computed tomography (CT) is widely used for suspected acute ischemic stroke. Radiologists often interpret these scans with limited information and may benefit from more complete knowledge of the clinical situation. Hypothesis: Providing detailed clinical information improves the interpretation of CTs for acute stroke. Methods: In the prospective Cornell AcutE Stroke Academic Registry (CAESAR), we randomly selected 100 patients who underwent noncontrast head CT within 6 hours of transient ischemic attack (TIA) or minor acute ischemic stroke (National Institutes of Health Stroke Scale score ≤3) and underwent magnetic resonance imaging (MRI) within 6 hours of the CT. Three radiologists each twice evaluated CT studies both with and without accompanying information on the patient’s medical history, neurological deficit, and symptom time course. In random sequence, each study was interpreted by each radiologist in one condition (i.e., with or without detailed accompanying information), and then after a 4-week washout period, the same study was interpreted again by each radiologist in the opposite condition. Using MRI diffusion weighted imaging (DWI) as the reference standard for brain infarction, we classified CT interpretations as correct (true positives or true negatives) or incorrect (false positives or false negatives). McNemar’s test was used to compare the proportion of correct interpretations in the condition with detailed clinical information versus the condition without detailed information. Results: In patients with DWI-defined infarcts, acute ischemia was correctly called on 20% (95% confidence interval [CI], 14-27%) of CTs with detailed history versus 18% (95% CI, 12-25%) without history. In patients without infarcts, the absence of acute ischemia was correctly called on 77% (95% CI, 70-84%) of CTs with history and 77% (95% CI, 69-83%) without history. The proportion of correct interpretations of CTs accompanied by detailed clinical history (49% [95% CI, 43-54%]) did not differ significantly from those without history (47% [95% CI, 42-53%]) ( P = 0.67). Conclusions: Reported findings on head CT for evaluation of suspected acute ischemic stroke were similar regardless of whether detailed clinical history was provided.
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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 | 0.013 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".