Digitized Infrared Segmental Thermometry: Time Requirements for Stable Recordings
Bibliographic record
Abstract
OBJECTIVES: Digitized infrared segmental thermometry (DIST) is a method for measuring and recording skin surface body temperatures. The project evaluated the required length of time for patients to acclimatize their core body temperature to ambient conditions to obtain stable DIST readings. METHODS: Seventeen subjects were allowed a 20-minute acclimatizing period in a temperature-controlled room. The bilateral DIST temperature was measured with thermistors in combination with infrared cameras (IRCs) at the C4 and L4 levels. All IRC temperatures were recorded after a 20-minute stabilization period. The room temperature and relative humidity were recorded throughout all trials. The acclimatization trend was computed from the 20- to 24-minute period for the IRCs, and the acclimatization trend was computed continuously for a total of 30 minutes (at 2-minute intervals) for 5 days. RESULTS: We discovered a stabilization trend in the early trial stages, with the thermistor recordings between 8 and 16 minutes. The IRC trend was also conclusive for the core temperature requirements. CONCLUSIONS: This study determined a core body temperature acclimatization trend tested among patients using thermistor recordings in a controlled environment. Based on these findings, we recommend acclimatization in a temperature- and humidity-controlled environment for a minimum 8-minute period, followed by an 8-minute maximum recording period with the patient in a prone position to obtain accurate DIST recordings.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".