Abstract T P323: Corticospinal Tract Disruption in Acute Intracerebral Hemorrhage is Unrelated to Baseline Hematoma Volume
Bibliographic record
Abstract
Background: Acute hematoma volume is the most powerful predictor of poor functional outcome after intracerebral hemorrhage (ICH). Putative reasons for functional deficits include disruption of white matter (WM) tracts. Fractional anisotropy (FA), as assessed with Diffusion Tensor Imaging (DTI), can be used to assess WM integrity. We tested the hypothesis that larger hematoma volumes are associated with lower FA values in the corticospinal tract (CST). Methods: We conducted a prospective cross-sectional imaging study in ICH patients with DTI scans within 7 days of symptom onset. Mean relative FA (rFA) was measured in the entire CST. Hematoma volume was measured using planimetric techniques on acute and follow-up CT scans. Motor function was assessed using a composite of the upper and lower extremity NIHSS subscale (0=normal, 8=hemiplegia) at day 7. Results: Twenty-one ICH patients (mean age 72±14) were imaged with DTI. Median hematoma volume was 9.6 (17.6) ml measured at the 1.8 (2.8) hour CT and 12.2 (35.2) ml at the 25.8 (28.9) hour CT. Median NIHSS motor score was 5 (7) at baseline and 3 (7) at day 7. DTI scans were grouped as acute (median time to imaging: 1.7 (0.78) days) and subacute (time to imaging: 6.9 (2.8) days). Mean acute FA was significantly lower in the CST ispsilateral to the hematoma (0.43±0.03) compared to the contralateral CST (0.48±0.03, p=0.002). Mean subacute FA was also lower in the ipsilateral (0.43 ± 0.04) vs. contralateral CST (0.60±0.15, p=0.002). Acute rFA (r= -0.33, p=0.68) and subacute rFA (r=-0.52, p=0.12) were not correlated with day 7 motor function. Acute hematoma volumes were not related to acute rFA (r= -0.33, p=0.35) or subacute rFA (r= -0.05, p=0.88). Patients with hematoma volumes <9ml had similar subacute rFA (0.96±0.05) to those with hematoma volumes >9ml (0.88±0.07, p=0.072). Acute rFA was similar between patients with hematomas directly adjacent to the CST (0.89±0.07) and those with hematomas separate from the CST (0.97±0.02, p=0.23). Conclusion: DTI demonstrates evidence of CST disruption in acute ICH. However, CST disruption appears to be independent of baseline hematoma volume. Serial assessment of rFA in ICH is required to further define the relationship between larger hematoma volume, rFA and functional outcome.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".