Time-Dependent Relationships Between Human Brain and Body Temperature After Severe Traumatic Brain Injury
Bibliographic record
Abstract
There is uncertainty about the reliability of using body temperature readings as a ‘surrogate’ measure of brain temperature. Aim: To determine the temporal inter-relationship between body and brain temperature after severe traumatic brain injury (TBI). Setting and Patients: Large University teaching hospital in the North West of England. Patients admitted for emergency neurocritical care. All patients received dual-modality monitoring of brain tissue pressure and temperature via invasive intracerebral micro-sensors. Body temperature was measured using an indwelling thermistor inserted in to the rectum. Methods: Temperature was monitored continuously with values stored to a bedside data acquisition system at intervals of 10 minutes. Data were transferred to a spreadsheet at end of each individual’s monitoring period for further analysis under Matlab routines. The method of functional principal components was used to determine the time-dynamics of brain and body temperature relationships. Results: In the period after severe TBI, median body and brain temperature for all readings and in all patients was 37.6oC and 37.7oC respectively; a statistical (p <0.001) but not clinically significant difference. A strong regression relationship between brain and body temperature was demonstrated (functional coefficient of determination, R2= 0.7623, p< 0.0020). Conclusions: Body temperature is a good early predictor of brain temperature but only during the first two days after severe TBI. The results will be of value for future predictive modeling of brain temperature changes, particularly where brain tissue monitoring is not clinically justified or available. In particular, results demonstrate the uncertainty in using body temperature as a surrogate for brain temperature beyond the first two days after severe traumatic brain injury.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".