A Non Invasive Wearable Sensor for the Measurement of Brain Temperature
Bibliographic record
Abstract
As the thermoregulation centres are deep in the brain, the cerebral temperature is one of the most important markers of fever, circadian rhythms physical and mental activities. However due to a lack of accessibility, the brain temperature is not easily measured. The axillary, buccal, tympanic and rectal temperatures do not reflect exactly the cerebral temperature. Nevertheless the rectal temperature is used as probably the most reliable indicator of the core body temperature. The brain temperature can be measured using NMR spectroscopy, microwave radiometry, near infrared spectroscopy, ultra-sound thermometry. However none of those methods are amenable to long term ambulatory use outside of the laboratory or of the hospital during normal daily activities, sport, etc. The brain core temperature "BCT" sensor, developed by the Biomedical Microsensors dpt of LPM at INSA-Lyon is a flexible active sensor using "zero-heat-flow" principle. The sensor has been used for experimental measurement: brain temperature during mental activity, and in hospital for the study of circadian rhythms. The results are in agreement with the measurement by the rectal probe. There are 2 versions of this sensor: a non ambulatory for the use in hospitals, and an ambulatory version using teletransmission. We are working for improving the autonomy of the ambulatory version up to several days. This wearable biomedical sensor (WBS) can be used for circadian assessment for chronobiology studies and in medical therapies
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".