Abstract WP113: Feasibility Of Non-invasive Brain Temperature Measurement In Acute Ischemic Stroke: A Comparison Study Of MR Thermometry Vs. Zero-Heat-Flux Sensors
Bibliographic record
Abstract
Introduction: Brain-selective hypothermia is a promising neuroprotectant in acute ischemic stroke. However, a non-invasive bedside method to monitor brain temperature during cooling is lacking. We tested the feasibility and accuracy of measuring brain temperature non-invasively using sensors designed to monitor core body temperature. Methods: In this prospective single-center study, 20 patients with large or medium vessel occlusion strokes were enrolled. Patients underwent a 3T MR spectroscopy imaging (MRSI, the reference standard) and zero heat flux (ZHF) core temperature sensor measurements (3M TM Bair Hugger TM ) within 12-72 hours from admission. Two ZHF sensors were placed on each side of the forehead on the side of stroke and contralateral side and the temperatures were checked before and after MRSI. A 2x2x2 cm voxel was centered on the infarct and matching contralateral location using diffusion weighted imaging. Brain temperature on MRSI was calculated using the relative chemical shifts of water and N-acetyl aspartate. Temperature measurements of the ZHF sensors were compared to MRSI measurements. Results: The brain temperature of stroke and contralateral sides were similar using MRSI or using ZHF sensors when each modality was compared to itself by hemisphere (all p>0.05). However, there was a significant difference for the ipsilateral side: median (SD) temperatures using MRSI was 36.0°C (1.8) vs 36.8°C (0.5) for sensor measurement. Bland-Altman plots showed that 95% of the observations fell within upper and lower limits of agreement of -2.1 to +3.7°C while 61% of observations fell within an agreement limit of +/- 1°C. There was an indication of an overall higher temperature measurements using the ZHF sensors compared to MRSI (Figure 1). Conclusions: More studies are needed to validate the use of ZHF thermometry in ischemic stroke including studies implementing hypothermia. Figure 1. Boxplot of stroke side MRSI compared to zero-heat-flux sensors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".