An update to the Monro–Kellie doctrine to reflect tissue compliance after severe ischemic and hemorrhagic stroke
Bibliographic record
Abstract
High intracranial pressure (ICP) can impede cerebral blood flow resulting in secondary injury or death following severe stroke. Compensatory mechanisms include reduced cerebral blood and cerebrospinal fluid volumes, but these often fail to prevent raised ICP. Serendipitous observations in intracerebral hemorrhage (ICH) suggest that neurons far removed from a hematoma may shrink as an ICP compliance mechanism. Here, we sought to critically test this observation. We tracked the timing of distal tissue shrinkage (e.g. CA1) after collagenase-induced striatal ICH in rat; cell volume and density alterations (42% volume reduction, 34% density increase; p < 0.0001) were highest day one post-stroke, and rebounded over a week across brain regions. Similar effects were seen in the filament model of middle cerebral artery occlusion (22% volume reduction, 22% density increase; p ≤ 0.007), but not with the Vannucci-Rice model of hypoxic-ischemic encephalopathy (2.5% volume increase, 14% density increase; p ≥ 0.05). Concerningly, this 'tissue compliance' appears to cause sub-lethal damage, as revealed by electron microscopy after ICH. Our data challenge the long-held assumption that 'healthy' brain tissue outside the injured area maintains its volume. Given the magnitude of these effects, we posit that 'tissue compliance' is an important mechanism invoked after severe strokes.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".