Prediction of persistent ventricular dilation by initial ventriculomegaly and clot volume in a porcine model
Bibliographic record
Abstract
OBJECTIVE: While intraventricular hemorrhage (IVH) is associated with posthemorrhagic ventricular dilation (PHVD), not all infants affected by high-grade IVH develop PHVD. The authors aimed to determine clot-associated predictors of PHVD in a porcine model by varying the amount and rate of direct intraventricular injection of whole autologous blood. METHODS: Seven 1-week-old piglets underwent craniectomy and injection of autologous blood into the right lateral ventricle. They survived for a maximum of 28 days. MRI was performed prior to injection, immediately postoperatively, and every 7 days thereafter. T1-weighted, T2-weighted, and susceptibility-weighted imaging (SWI) sequences were used to segment ventricular and clot volumes. Spearman correlations were used to determine the relationship between blood and clot volumes and ventricular volumes over time. RESULTS: The maximum ventricular volume was up to 12 times that of baseline. One animal developed acute hydrocephalus on day 4. All other animals survived until planned endpoints. The interaction between volume of blood injected and duration of injection was significantly associated with clot volume on the postoperative scan (p = 0.003) but not the amount of blood injected alone (p = 0.38). Initial postoperative and day 7 clot volumes, but not volume of blood injected, were correlated with maximum (p = 0.007 and 0.014) and terminal (p = 0.014 and 0.036) ventricular volumes. Initial postoperative ventricular volume was correlated with maximum and terminal ventricular volume (p = 0.007 and p = 0.014). CONCLUSIONS: Initial postoperative, maximum, and terminal ventricular dilations were associated with the amount of clot formed, rather than the amount of blood injected. This supports the hypothesis that PHVD is determined by clot burden rather than the presence of blood products and allows further testing of early clot lysis to minimize PHVD risk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".