One size does not fit all: Assuming the same normal body temperature for everyone is not justified
Bibliographic record
Abstract
Despite the increasing personalization of medicine, surprisingly ~37.0°C (98.6°F) continues as the estimate of normal temperature. We investigated between-subject and within-subject thermal variability, whether a significant percentage of individuals have a low mean oral temperature, and whether these differ by sex, age, time of day, ethnicity, body mass index (BMI), or menstrual phase. Oral temperature was measured by Life Brand® Fast-Read Digital Oral Thermometers and sampled 14 times over two weeks, seven morning and seven evening readings. The volunteer sample consisted of 96 adults (42 men, 54 women; 27 couples, 42 singletons), ages 18-67 years. We found sizeable individual differences in body temperature and that the normal temperature of many individuals is considerably lower than 37.0°C (98.6°F). Mean temperatures ranged from 35.2°C (95.4°F) to 37.4°C (99.3°F). The mean temperature across all participants was 36.1°C (97.0°F)-lower than most studies have reported, consistent with recent evidence of temperature declining over decades. 77% had mean temperatures at least 0.55°C (1°F) lower than 37.0°C (98.6°F). Mean temperature did not differ by age, but women had higher temperatures than men, even within a couple with room temperature and warmth of clothing equated. Although oral temperature varied widely across individuals, it showed marked stability within individuals over days. Variability of temperature over days did not differ by sex, but was larger among younger adults. Using 37.0°C (98.6°F) as the assumed normal temperature for everyone can result in healthcare professionals failing to detect a serious fever in individuals with a low normal temperature or obtaining false negatives for those individuals when using temperature to screen for COVID-19, mistaking their elevated temperature as normal. Some have called for lowering the estimate of normal temperature slightly (e.g., 0.2°C [0.36°F]). That still seems an overly high estimate. More important, using any standardized "normal" temperature will lead to errors for many people. Individual differences are simply too great. Personalizing body temperature is needed. Temperature could be measured at yearly doctor visits, as blood pressure is now. That would be simple to implement. Since our results show marked thermal stability within an individual, sampling temperature only once yearly could provide an accurate indication of a person's normal temperature at that time of day. Such records over time would also provide a more accurate understanding of how temperature changes over the lifespan.
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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 | 0.128 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.006 | 0.023 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".