Three Types of Skin-Surface Thermometers
Bibliographic record
Abstract
OBJECTIVE: To compare the reliability, validity, and responsiveness of a thermistor thermometer (thermistor) and two different infrared thermometers (one designed to measure tympanic temperature and one for skin temperature). DESIGN: Reliability and validity were evaluated by making two separate measurements from the skin at identical spots of each hand, forearm, shoulder, thigh, shin, and foot in 17 healthy subjects. Intramuscular temperature was recorded at the hand and shin sites. Test-retest reliability was calculated using intraclass correlation for each instrument. Pearson correlation assessed the relationship between the skin and intramuscular temperatures at the hand and shin sites (validity). Each instrument's ability to measure temperature change (responsiveness) was assessed by measuring skin temperatures serially from 17 limbs of ten patients with complex regional pain syndrome undergoing intravenous regional sympathetic blockade. Responsiveness index values were calculated. RESULTS: Reliability was strong and similar for each device (intraclass correlation: thermistor = 0.96, tympanic = 0.96, skin = 0.97), as was validity (r: thermistor = 0.90, tympanic = 0.92, skin = 0.92). Responsiveness was marginally better for the infrared skin device (responsiveness index: skin = 4.2, tympanic = 3.6, thermistor = 3.6). CONCLUSIONS: For the purposes of clinical electrodiagnostic laboratory and other physiatry applications, the performance of the infrared thermometers is equal to or superior to that of the traditionally used thermistor. All three devices are highly reliable and valid, whereas the infrared skin device is slightly more responsive. Infrared thermometers have the advantage of being quicker to operate and more portable.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".