Multicenter Accuracy and Interobserver Agreement of Spot Sign Identification in Acute Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Rapid, accurate, and reliable identification of the computed tomography angiography spot sign is required to identify patients with intracerebral hemorrhage for trials of acute hemostatic therapy. We sought to assess the accuracy and interobserver agreement for spot sign identification. METHODS: A total of 131 neurology, emergency medicine, and neuroradiology staff and fellows underwent imaging certification for spot sign identification before enrolling patients in 3 trials targeting spot-positive intracerebral hemorrhage for hemostatic intervention (STOP-IT, SPOTLIGHT, STOP-AUST). Ten intracerebral hemorrhage cases (spot-positive/negative ratio, 1:1) were presented for evaluation of spot sign presence, number, and mimics. True spot positivity was determined by consensus of 2 experienced neuroradiologists. Diagnostic performance, agreement, and differences by training level were analyzed. RESULTS: Mean accuracy, sensitivity, and specificity for spot sign identification were 87%, 78%, and 96%, respectively. Overall sensitivity was lower than specificity (P<0.001) because of true spot signs incorrectly perceived as spot mimics. Interobserver agreement for spot sign presence was moderate (k=0.60). When true spots were correctly identified, 81% correctly identified the presence of single or multiple spots. Median time needed to evaluate the presence of a spot sign was 1.9 minutes (interquartile range, 1.2-3.1 minutes). Diagnostic performance, interobserver agreement, and time needed for spot sign evaluation were similar among staff physicians and fellows. CONCLUSIONS: Accuracy for spot identification is high with opportunity for improvement in spot interpretation sensitivity and interobserver agreement particularly through greater reliance on computed tomography angiography source data and awareness of limitations of multiplanar images. Further prospective study is needed.
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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 | 0.035 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".