A Systematic Review of Depth-Dependent Cytoprotection with Therapeutic Hypothermia for Cerebral Ischemia
Bibliographic record
Abstract
Preclinical studies show that therapeutic hypothermia (TH) effectively reduces cerebral ischemic injury. In contrast, TH has not been consistently beneficial in clinical trials of stroke and cardiac arrest, perhaps from suboptimal dosing (e.g., delay, depth, and duration), among other factors. This systematic review aimed to find an optimal depth of TH from in vivo adult preclinical studies of global and focal ischemia. To study depth, without other confounds, we examined studies that compared ≥2 depths of TH versus normothermic controls. Our primary outcomes were infarct size (focal ischemia) and hippocampal cell death (global ischemia), while secondary outcomes were behavior, edema, and striatal cell death. Studies were assessed with the SYRCLE Risk of Bias tool (e.g., use of blinding) and additional indices of translational rigor (e.g., use of aged animals). Thirty studies were included from a search of the PubMed database in 2025. Many studies were rated as exhibiting a high risk of bias with low translational rigor. Overall, TH provided considerable protection on all endpoints, sometimes up to 100%, but no consistent dose–response patterns emerged, nor was an optimal depth of cooling readily evident. To explore the latter finding, specifically sampling variability, we conducted Monte Carlo simulations using the pooled standard deviation of the preclinical studies to generate three populations based upon a theoretical 5% protection per 1°C relationship (37°C vs. 32°C vs. 27°C groups run 75 times). Dose-dependent effects were statistically detectable in only 36% of comparisons, which showed comparably noisy patterns of protection. Thus, the variable dose-dependent effects in the reviewed animal studies likely arise, at least partially, from sampling error owing to using small samples from variable populations (average n = 8/group in focal ischemia). Overall, these findings highlight weaknesses in the extant dose–response literature that limit our ability to precisely guide clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".