Accuracy of Peripheral Thermometers for Estimating Temperature
Bibliographic record
Abstract
BACKGROUND: Body temperature is commonly used to screen patients for infectious diseases, establish diagnoses, monitor therapy, and guide management decisions. PURPOSE: To determine the accuracy of peripheral thermometers for estimating core body temperature in adults and children. DATA SOURCES: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and CINAHL Plus from inception to July 2015. STUDY SELECTION: Prospective studies comparing the accuracy of peripheral (tympanic membrane, temporal artery, axillary, or oral) thermometers with central (pulmonary artery catheter, urinary bladder, esophageal, or rectal) thermometers. DATA EXTRACTION: 2 reviewers extracted data on study characteristics, methods, and outcomes and assessed the quality of individual studies. DATA SYNTHESIS: 75 studies (8682 patients) were included. Most studies were at high or unclear risk of patient selection bias (74%) or index test bias (67%). Compared with central thermometers, peripheral thermometers had pooled 95% limits of agreement (random-effects meta-analysis) outside the predefined clinically acceptable range (± 0.5 °C), especially among patients with fever (-1.44 °C to 1.46 °C for adults; -1.49 °C to 0.43 °C for children) and hypothermia (-2.07 °C to 1.90 °C for adults; no data for children). For detection of fever (bivariate random-effects meta-analysis), sensitivity was low (64% [95% CI, 55% to 72%]; I2 = 95.7%; P < 0.001) but specificity was high (96% [CI, 93% to 97%]; I2 = 96.3%; P < 0.001). Only 1 study reported sensitivity and specificity for the detection of hypothermia. LIMITATIONS: High-quality data for some temperature measurement techniques are limited. Pooled data are associated with interstudy heterogeneity that is not fully explained by stratified and metaregression analyses. CONCLUSION: Peripheral thermometers do not have clinically acceptable accuracy and should not be used when accurate measurement of body temperature will influence clinical decisions. PRIMARY FUNDING SOURCE: None.
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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 | 0.084 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".