Abstract 114: The Predictive Ability of the CTA Spot Sign for Hematoma Expansion is Dependent on Time Since ICH Onset: A Systematic Review and Patient-level Meta-analysis.
Bibliographic record
Abstract
Background: Hematoma expansion (HE) occurs in up to 40% of patients with intracerebral hemorrhage (ICH), and predicts poor clinical outcome. Contrast extravasation following CT-angiography (CTA), termed “spot sign”, identifies patients at highest risk of HE. However, the prevalence and predictive values of the spot sign varies across studies, possibly due to differences in onset-to-CTA time. We therefore performed a patient-level meta-analysis to define the relationship between onset-to-CTA time and the prevalence & predictive value of spot sign, and the size of HE. Methods: We searched the Cochrane Central Register of Controlled Trials, the Cochrane Library Database of Systematic Reviews, MEDLINE and EMBASE for studies of CTA spot sign prevalence and HE. We pooled data on the prevalence and predictive values for significant HE (defined as either 6mL or 33% growth of ICH) for patients with ICH stratified by onset-to-CTA time: <3hours, 3-6 hours, >6hours. We used chi-square analysis to assess the spot sign in each time strata, and two-way ANOVA to compare across time strata. Results: We identified ICH spot sign databases derived from 7 countries and 14 centers (n=705). Prevalence of spot sign decreased with increasing onset-to-CTA time (Table; p<0.001). The subset with follow-up scans used for HE analysis (n=582) revealed spot sign sensitivity and PPV were highest in the earliest time strata, whereas specificity and NPV were highest in the latest time strata (Table). Spot positive patients had greatest absolute HE in the earlier CTA time strata (median spot positive growth 6.8mL, 5.6mL, 5.2mL for 6hr respectively; p<0.001; means in Table). Conclusion: Prevalence, predictive values and magnitude of effect of the spot sign are dependent on onset-to-CTA timing; these results are relevant to both ICH trial design and acute management.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".