Increased Prognostic Yield by Combined Assessment of Non-Contrast Computed Tomography Markers of Antithrombotic-Related Spontaneous Intracerebral Hemorrhage Expansion
Bibliographic record
Abstract
Background and aims: The utility of proposed non-contrast computed tomography (NCCT) markers for the prediction of hematoma expansion in patients with antithrombotic-related spontaneous intracerebral hemorrhage (ICH) is limited. Additionally, there is significant overlap between different suggested ICH shape and density markers. Methods: We assessed the prognostic yield for hematoma expansion of a combined score incorporating features of ICH shape irregularity (satellite sign and/or Barras score ≥ 3), heterogeneous ICH density (swirl sign and/or Barras score ≥ 3) on baseline NCCT and timing from ICH onset to NCCT. Results: We evaluated data from 79 patients with antithrombotic-related spontaneous ICH (32% with hematoma expansion). Swirl (84% vs. 39%) and satellite signs (20% vs. 7%) on baseline NCCT were significantly more prevalent (p < 0.001) in patients with hematoma expansion. Patients with hematoma expansion had more irregular and heterogeneous bleeds on baseline NCCT scans, as quantified by higher (p < 0.001) Barras shape (4 (4−5) vs. 3 (2−4)) and density scores (4 (3−5) vs. 2 (1−3)), respectively. The overall diagnostic yield of the combined score (area under the curve: 0.86, 95%CI: 0.78−0.94) significantly outperformed (p < 0.001) the diagnostic yield of each individual marker. Scores of 4 or 5 in the combined score were associated with a sensitivity of 60.0%, specificity of 90.7%, overall diagnostic accuracy of 81.0%, positive likelihood ratio (LR) of 6.48, negative LR of 0.44, positive predictive value (PV) of 0.76 and negative PV of 0.83. Conclusion: Combined NCCT marker assessment seems to increase the prognostic accuracy for hematoma expansion in antithrombotic-related spontaneous ICH patients.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".