Targeting focal ischemic and hemorrhagic stroke neuroprotection: Current prospects for local hypothermia
Bibliographic record
Abstract
Therapeutic hypothermia (TH) has applications dating back millennia. In modern history, however, TH saw its importation into medical practice where investigations have demonstrated that TH is efficacious in ischemic insults, notably cardiac arrest and hypoxic-ischemic encephalopathy. As well, studies have been undertaken to investigate whether TH can provide benefit in focal stroke (i.e., focal ischemia and intracerebral hemorrhage). However, clinical studies have encountered various challenges with induction and maintenance of post-stroke TH. Most clinical studies have attempted to use body-wide cooling protocols, commonly hindered by side effects that can worsen post-stroke outcomes. Some of the complications and difficulties with systemic TH can be circumvented by using local hypothermia (LH) methods. Additional advantages include the potential for lower target temperatures to be achieved and faster TH induction rates with LH. This systematic review summarizes the body of clinical and preclinical LH focal stroke studies and raises key points to consider for future LH research. We conclude with an overview of LH neuroprotective mechanisms and a comparison of LH mechanisms with those observed with systemic TH. Overall, whereas many LH studies have been conducted preclinically in the context of focal ischemia, insufficient work has been done in intracerebral hemorrhage. Furthermore, key translational studies have yet to be done in either stroke subtype (e.g., varied models and time-to-treat, studies considering aged animals or animals with co-morbidities). Few clinical LH investigations have been performed and the optimal LH parameters to achieve neuroprotection are unknown.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".