P.177 Assessing lesion severity in a mouse model of intracerebral hemorrhage
Bibliographic record
Abstract
Background: Intracerebral hemorrhage (ICH) is caused by the rupture of a blood vessel, resulting in bleeding into the brain. This type of stroke accounts for 12-15% of all strokes and is the most fatal type of stroke (40% mortality rate). ICH stroke presents a greater burden to survivors as 90% of ICH survivors have some degree of disability. Because of the poor patient outcomes, there is a clear need for identifying new treatments as well as establishing the efficacy of existing treatments. Methods: Using previously described techniques we aim to establish a mouse model of ICH at the University of Saskatchewan. We observed them at a 1- 24-, 48- and 72hr mark post-stroke and scored for neurological deficits. Results: We currently have conducted 23 model trials. We have data on mice at 1-, 24, and 72 hr post-stroke. We have observed and begun to analyze a lesion area that can be seen in H&E stained sections, and further assessed with FTIR, and synchrotron modalities. Conclusions: We have analyzed several of the specimens with FTIR imaging and synchrotron analysis with the Canadian Light Source. We have preliminary results of the progression of metabolites in the hematoma and peri-hematoma zone of the ICH.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".