Eye Position Information on CT Increases the Identification of Acute Ischemic Hypoattenuation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: It is possible that identification of eye deviation may sensitize a scan reader to early brain hypodensity associated with an arterial occlusive process. Our aim was to investigate the value of observing eye deviation on blinded CT identification of early hypoattenuation following ischemic infarct. MATERIALS AND METHODS: Two staff and 2 fellow neuroradiologists reviewed 75 brain CT scans obtained within 3 hours of acute ischemia from subjects in the Interventional Management of Stroke Study. Films were reviewed 3 months apart, the first time with tape over the eyes on the images, the second with the eyes visible. Readers were asked if early hypoattenuation in the middle cerebral artery (MCA) distribution or if a hyperattenuated MCA was present. kappa statistics were calculated to determine agreement among the 4 readers and between each of the 2 readings by the same reader, not only for the original interpretation of the blinded study neuroradiologist but also for the Alberta Stroke Program Early CT Score (ASPECTS) for each subject assigned by an unblinded expert panel. A generalized estimating equations modeling approach was used to look at the overall effect of including eye information for agreement between interpretations. RESULTS: Eye information availability was associated with improved agreement for detection of early ischemic hypoattenuation not only among the 4 readers but also between the 4 readers and both the blinded study neuroradiologist (P = .02) and the unblinded expert ASPECTS panel. When comparing first and second readings for hypoattenuation, we also noted increased mean values for sensitivity (46.8% first, 56.5% second), specificity (78.2%, 80.2%), positive predictive value (72.0%, 80.7%), negative predictive value (55.5%, 61.0%), and percentage agreement (61.0%, 67.5%). CONCLUSION: Observation of CT eye deviation significantly improves reader identification of acute ischemic hypoattenuation.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".