Swirls and spots: relationship between qualitative and quantitative hematoma heterogeneity, hematoma expansion, and the spot sign
Bibliographic record
Abstract
Acute intracerebral hemorrhage (ICH) heterogeneity on NCCT, characterized by qualitative and quantitative methods, is predictive of hematoma expansion and mortality however association with the spot sign is not well-described. We sought to validate and determine the association between qualitative and quantitative hematoma heterogeneity with expansion and the spot sign, respectively. We retrospectively studied 71 ICH patients presenting <24 h post-ictus with baseline NCCT, CTA and 24-hour follow-up CT available. Baseline NCCT was assessed qualitatively for presence of swirl sign or hematoma heterogeneity by two independent readers blinded to CTA findings and quantitatively using CT densitometry (CTD). Associations with 24-hour hematoma expansion ≥6 ml or ≥33 % and spot sign were assessed using logistic regression and diagnostic performance was assessed. Association between qualitative and quantitative densitometry parameters was also examined. Swirl sign and quantitative CTD standard deviation were independently associated with expansion on multivariable analysis ( p = 0.037 and p = 0.032, respectively). Swirl sign and hematoma heterogeneity were predictive of CTA spot sign ( p = 0.020 and p = 0.035, respectively) while CTD standard deviation demonstrated only trend univariate association. CTD parameters were not significantly associated with swirl sign while only CTD skewness was associated with hematoma heterogeneity. Agreement for swirl sign and hematoma heterogeneity identification was nearly perfect (κ = 0.81) and substantial (κ = 0.79) respectively. NCCT qualitative parameters predict hematoma expansion and CTA spot sign presence. Quantitative markers independently predict hematoma expansion but not CTA spot sign presence.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".