Planimetric Hematoma Measurement in Patients With Intraventricular Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Reliable quantification of both intracerebral hemorrhage and intraventricular hemorrhage (IVH) volume is important for hemostatic trials. We evaluated the reliability of computer-assisted planimetric volume measurements of IVH. METHODS: Computer-assisted planimetry was used to quantify IVH volume. Five raters measured IVH volumes, total (intracerebral hemorrhage+IVH) volumes, and Graeb scores from 20 randomly selected computed tomography scans twice. Estimates of interrater and intrarater reliability were calculated and expressed as an intrarater correlation coefficient and an absolute minimum detectable difference. RESULTS: Planimetric IVH volume analysis had excellent intra- and interrater agreement (intrarater correlation coefficient, 0.96 and 0.92, respectively), which was superior to the Graeb score (intrarater correlation coefficient, 0.88 and 0.83). Minimum detectable differences for intra- and interrater volumes were 12.1 mL and 17.3 mL, and were dependent on the total size of the hematoma; hematomas smaller than the median 43.8 mL had lower minimum detectable differences, whereas those larger than the median had higher minimum detectable differences. Planimetric total hemorrhage volume analysis had the best intra- and interrater agreement (intrarater correlation coefficient, 0.99 and 0.97, respectively). CONCLUSIONS: Computer-assisted planimetric techniques provide a reliable measurement of ventricular hematoma volume, but are susceptible to higher absolute error when assessing larger hematomas.
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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 | 0.014 |
| 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) | 0.001 | 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".