The Complex Relationship Between Cooling Parameters and Neuroprotection in a Model of Selective Hypothermia
Bibliographic record
Abstract
Background Hypothermia remains the best studied neuroprotectant. Despite extensive positive large and small animal data, side effects continue to limit human applications. Selective hypothermia is an efficient way of applying neuroprotection to the brain without the systemic complications of global hypothermia. However, optimal depth and duration of therapeutic hypothermia are still unknown. We analyzed a large animal cohort study of selective hypothermia for statistical relationships between depth or duration of hypothermia and the final stroke volume. Methods A cohort of 30 swine stroke subjects provided the dataset for normothermic and selective hypothermic animals. Hypothermic parameters including duration, temperature nadir, and an Area Under the Curve measurement for 34 and 30°C were correlated with the final infarct volumes measured by MRI and histology. Results Between group comparisons continue to demonstrate a reduction in infarct volume with selective hypothermia. Histologically-derived infarct volumes were 1.2 mm3 smaller in hypothermia-treated pigs (P = 0.04) and showed a similar, but non-significant reduction in MRI (P = 0.15). However, within the selective hypothermia group, more intense cooling, as measured through increased AUC 34 and decreased temperature nadir was associated with larger infarct proportions by MRI [Pearson's r = 0.48 (p = 0.05) and r = −0.59 (p = 0.01), respectively]. Reevaluation of the entire cohort with quadratic regression demonstrated a U-shaped pattern, wherein the average infarct proportion was minimized at 515 degree-minutes (AUC34) of cooling, and increased thereafter. In a single case of direct brain tissue oxygen monitoring during selective hypothermia, brain tissue oxygen strongly correlated with brain temperature reduction over the course of selective hypothermia to 23°C. Conclusions In a large animal model of selective hypothermia applied to focal ischemia, there is a non-monotone relationship between duration and depth of hypothermia and stroke volume reduction. This suggests a limit to depth or duration of selective hypothermia for optimal neuroprotection. Further research is required to delineate more precise depth and duration limits for selective hypothermia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".