Factors Influencing the Detection of Early CT Signs of Cerebral Ischemia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Early CT signs of cerebral ischemia are subtle. Little is known of which factors influence the detection of infarct signs. We compared neuroradiologists' scan readings with those of other specialists involved in the care of stroke patients. METHODS: We used the Internet to show 63 CT scans, all acquired <6 hours after stroke and representing different patient ages, times to scanning, stroke severity, and early CT signs of ischemia to physicians involved in stroke care. They completed a structured scan interpretation proforma over the Internet. We compared the detection of early ischemic signs stratified by severity and the effect of prior stroke between different specialties. RESULTS: Among 207 observers, neuroradiologists saw significantly more of "any early ischemic changes" than did stroke physicians, general radiologists, geriatricians, or neurologists (all P<0.0001), predominantly due to neuroradiologists' greater detection of "mild" hypoattenuation or swelling. Detection of "severe" hypoattenuation or swelling, and hyperattenuated arteries did not differ between specialties. Old infarcts impaired recognition of early ischemic signs. Non-neuroradiologists did not "over-call" signs. Years of scan-reading experience did not account for these differences, but neuroradiologists took, on average, 30 seconds longer to read each scan than did most other specialists (P<0.0001). CONCLUSIONS: Non-neuroradiologists should realize that they are unlikely to over-call signs, that old infarcts may distract them from seeing early ischemic signs, and read stroke CT scans more slowly, as these factors may help them perform more like neuroradiologists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".