Magnitude of Hematoma Volume Measurement Error in Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Limiting intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) expansion is a common target for acute ICH studies and, therefore, accurate measurement of hematoma volumes is required. We investigated the amount of hematoma volume difference between computed tomography scans that can be considered as measurement error. METHODS: Five raters performed baseline (<6 hours) and 24-hour total hematoma (ICH+IVH) computer-assisted volumetric analysis from 40 selected ICH patients from the Predicting Hematoma Growth and Outcome in Intracerebral Hemorrhage Using Contrast Bolus CT (PREDICT) study cohort twice. Estimates of intrarater and interrater reliability are expressed as intraclass correlation coefficients and minimum detectable difference (MDD). RESULTS: Total hematoma volumetric analyses had excellent intra- and interrater agreements (intraclass correlation coefficients 0.994 and 0.992, respectively). MDD for intra- and interrater volumes was 6.68 and 7.72 mL, respectively, and were higher the larger total hematoma volume was and in patients with subarachnoid hemorrhage or IVH. MDD for total hematoma volume measurement of 10.4 mL was found in patients with largest hematoma volumes. In patients with subarachnoid hemorrhage or IVH, MDD for total hematoma volume was 10.3 and 10.4 mL, respectively. In patients without IVH, MDD for intra- and interrater pure ICH volumes were 3.82 and 5.83 mL, respectively. CONCLUSIONS: A threshold higher than 10.4 mL seems to be reliable to avoid error of total hematoma volume measurement in a broad range of patients. An absolute ICH volume increase of >6 mL, commonly used as outcome in ICH studies, seems well above MDD and, therefore, could be used to reliably detect ICH expansion.
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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 | 0.031 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".