Temperature Control in Rodent Neuroprotection Studies: Methods and Challenges
Bibliographic record
Abstract
Extensive animal research facilitated the clinical translation of therapeutic hypothermia for cardiac arrest in adults and hypoxic-ischemic injury in infants. Similarly, clinical interest in hypothermia for other brain injuries, such as stroke, has been greatly supported by positive findings in preclinical work. The reliability, validity, and utility of animal models, among many research practices (blinding, randomization, etc.), are key to successful clinical translation. Here, we review methods used to induce and maintain hypothermia in animal models. These include physical and pharmacological methods. We emphasize the advantages and limitations of each approach, and the importance of using clinically relevant cooling protocols and appropriate monitoring and reporting approaches. Moreover, we performed a literature survey of ischemic stroke studies published in 2015 to highlight the continuing risk of temperature confounds in neuroprotection studies. For example, many still do not accurately monitor and report temperature during surgery (23.5%), even though almost half of these studies (46.0%) use pharmaceutical agents that likely influence temperature. We hope this review stimulates awareness and discussion of the importance of temperature in neuroprotective studies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".