Model of minor stroke with mild peri-infarct ischemic injury
Bibliographic record
Abstract
BACKGROUND: Transient ischemic attack, minor stroke and stroke recurrence need improved treatment but lack animal models for research. The aim was to modify photothrombosis methods thereby producing both a minor stroke (with adjacent mild damage) or a minor recurrent stroke. NEW METHOD: A minor stroke, as detected using magnetic resonance imaging and histology, was produced using a low intensity beam of white light with a bright centre, a low dose of Rose Bengal and a short 5min illumination of thinned skull. A recurrent minor stroke was produced by repeating the procedure two days later except the cortical mask was positioned 1.5mm posteriorly. RESULTS: The minor photothrombosis procedure produced a small superficial infarct surrounded by a region of scattered necrosis detected histologically. Marked hyperintensities in diffusion weighted and T2 images identified the infarct. Peri-infarct regions with modest T2 increases corresponded to regions of scattered cell death. A recurrent minor photothrombosis produced additional damage in regions with overlapping mild injury. COMPARISON WITH EXISTING METHODS: Previous photothrombosis methods usually produce large cortical infarcts with little penumbra. The current method produces small infarcts with diffuse mild peri-infarct ischemic injury that can be diagnosed using T2 imaging. CONCLUSIONS: The modified photothrombotic procedure will produce a minor stroke consisting of a small infarct in a region with marked diffusion and T2 hyperintensities and a peri-infarct region of selective necrosis with modest T2 changes. Minor recurrent stroke is readily produced but imaging is key for assessing size and location of each insult.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".