Interobserver variability in the interpretation of computed tomography following aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECT: Numerous abnormal findings may be evident on CT scans after aneurysmal subarachnoid hemorrhage (SAH). Here, the authors assess the interobserver variability in the radiological interpretation of the initial CT scan following SAH. METHODS: Two experienced reviewers, a neurosurgeon and a neuroradiologist, independently prospectively reviewed the initial CT scans of 413 patients enrolled in the CONSCIOUS-1 trial. Measured variables included SAH, intraventricular hemorrhage, intracerebral hemorrhage, subdural hematoma, chronic infarction, midline shift, and hydrocephalus. To assess interobserver variability, weighted kappa values and intraclass correlation coefficients (ICCs) were calculated and Bland-Altman analysis was performed. RESULTS: Moderate to substantial agreement was found for most of the CT scanning findings. There was fair to moderate interobserver agreement between reviewers when determining the extent of SAH based on a descriptive categorical classification (kappa 0.41; 95% CI 0.33-0.49), and better agreement when a semiquantitative scale was used (ICC 0.56; 95% CI 0.49-0.62). There was poor agreement between reviewers for the presence of hydrocephalus (kappa 0.34; 95% CI 0.20-0.48), but substantial to near perfect agreement on ventriculocranial ratio measurements (ICC 0.77; 95% CI 0.72-0.81). CONCLUSIONS: The authors' findings suggest that there is considerable interobserver variability in the interpretation of CT scans after SAH. Quantitative measures may reduce interobserver variability in comparison with qualitative or categorical scales. Variability in interpretation of CT scans has implications for patient care and conduct of clinical trials. It may be beneficial to develop standardized assessments to ensure consistent evaluation of measured variables.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".