Examining potential side effects of therapeutic hypothermia in experimental intracerebral hemorrhage
Bibliographic record
Abstract
Studies treating intracerebral hemorrhage (ICH) with therapeutic hypothermia (TH) have shown inconsistent benefits. We hypothesized that TH's anti-inflammatory effects may be responsible as inflammatory cells are essential for removing degrading erythrocytes. Here, we subjected rats to a collagenase-induced striatal ICH followed by whole-body TH (∼33℃ for 11-72 h) or normothermia. We used X-ray fluorescence imaging to spatially quantify total and peri-hematoma iron three days post-injury. At three and seven days, we measured non-heme iron levels. Finally, hematoma volume was quantified on one, three, and seven days. In the injured hemisphere, total iron levels were elevated ( p < 0.001) with iron increasing in the peri-hematoma region ( p = 0.007). Non-heme iron increased from three to seven days (p < 0.001). TH had no effect on any measure of iron ( p ≥ 0.479). At one and three days, TH did not affect hematoma volume ( p ≥ 0.264); however, at seven days there was a four-fold increase in hematoma volume in 40% of treated animals ( p = 0.032). Thus, even when TH does not interfere with initial increases in total and non-heme iron or its containment, TH can cause re-bleeding post-treatment. This serious complication could partly account for the intermittent protection previously observed. This also raises serious concerns for clinical usage of TH for ICH.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".