DOI: 10.5061/dryad.3tx95x6mt
title: Data for: Defining usual oral temperature ranges in outpatients using an unsupervised learning algorithm

description: Importance: Although oral temperature is commonly assessed in medical
 examinations, the range of usual or “normal” temperature is poorly
 defined. Objective: To determine normal oral temperature ranges by age,
 sex, height, weight and time of day. Design: We applied a filtering
 algorithm (LIMIT) to 10 years of outpatient temperature measurements.
 LIMIT iteratively removed encounters with primary diagnoses
 overrepresented in the tails of the temperature distribution, leaving only
 those diagnoses unrelated to temperature. Mixed effects modeling was
 applied to the remaining temperature measurements to identify independent
 predictors of normal oral temperature and to generate individualized
 normal temperature ranges. Setting: Single large medical care system,
 divisions of Internal Medicine and Family Medicine. Participants: All
 adult outpatient encounters that included temperature measurements, April
 2008 - June 2017. Exposures: Primary diagnoses and medications,
 age, sex, height, weight, time of day and month, abstracted from each
 outpatient encounter. Main outcomes and measures: Normal temperature
 ranges by age, sex, height, weight, and time of day. Results: From 618,306
 encounters, 36% were removed by LIMIT because they included diagnoses or
 medications that fell disproportionately in the tails of the temperature
 distribution. The encounters removed due to overrepresentation in the
 upper tail were primarily linked to infectious diseases (76.81% of all
 removed encounters); type 2 diabetes mellitus was the only diagnosis
 removed for over-representation in the lower tail (15.71% of all removed
 encounters). Prior to running LIMIT, the mean overall oral temperature was
 36.71°C (±0.43); following LIMIT, the mean temperature was 36.64°C
 (±0.35). Using mixed effects modeling, age, sex, height, weight and time
 of day accounted for 6.86% (overall) and up to 25.52% (per subject) of the
 observed variability in temperature. Mean normal oral temperature did not
 reach 37°C for any subgroup; the upper 99th percentile ranged from 36.8°C
 (tall underweight old men in the morning) to 37.9°C (short obese young
 women in the afternoon). Conclusion and relevance: Normal oral temperature
 varies in a predictable manner based on sex, age, height, weight and time
 of day, allowing individualized normal temperature ranges to be
 established. 
description: From the Stanford Research Repository (STARR, also known as
 STRIDE), we identified all adult outpatient encounters that included
 temperature measurements from April 2008 through June 2017 in the
 Divisions of Internal Medicine and Family Medicine within Stanford Health
 Care (Stanford, CA). Oral temperature, the date and time of the
 temperature measurement, age, sex, weight, height, body mass index (BMI),
 primary reason for the visit, prescribed medications, and all visit ICD-10
 codes were identified from each encounter. An individual patient could
 have multiple encounters. After identifying ineligible
 encounters in STARR, we applied a filtering algorithm, LIMIT, which
 iteratively removed encounters with primary diagnoses overrepresented in
 the tails of the temperature distribution, leaving only those diagnoses
 unrelated to temperature. Three subsets are specified: 
 (1) "Baseline" or STARR/STRIDE encounters (those excluded from
 consideration by LIMIT due to extreme or missing variables, or highly
 skewed values (temperatures less than 34°C (93.2°F) or over 40°C (104°F),
 age less than 20 or over 80 years, BMI less than 10 or over 50, height
 less than 1.37m (4’6”) or greater than 2.13m (6’10”), weight less than
 27.22 kg (60.0lbs) or greater than 181.44 kg (400lbs)). (2) The
 "outlier" set of encounters removed by LIMIT. (3) The
 "analysis" set of encounters with usual or "normal"
 temperature values as determined by LIMIT. Three files
 describing code are included with the data and the README file: Section A
 SAS; Section B Temperature modeling (in R); Section C LIMIT (in R). The
 dataset anon_AnalysisDataV2.csv includes a reduced set of variables to
 meet the guidelines for properly anonymized human subject data. Empty
 cells are intentional, representing no data in the modified dataset, due
 either to values removed for anonymization purposes or to no actual
 value.
description: The datafile is a compressed CSV file.