Agreement between the ventilated capsule and the KuduSmart® device for measuring sweating responses to passive heat stress and exercise
Bibliographic record
Abstract
The present study assessed agreement between a wireless sweat rate monitor (KuduSmart® device) and the ventilated capsule (VC) technique for measuring: (i) minute-averaged local sweat rate (LSR), (ii) sweating onset, (iii) sudomotor thermosensitivity, and (iv) steady-state LSR, during passive heat stress and exercise. It was hypothesized that acceptable agreement with no bias would be observed between techniques for all assessed sweating characteristics. On two separate occasions for each intervention, participants were either passively heated by recirculating hot water (49 °C) through a tube-lined garment until rectal temperature increased 1 °C over baseline ( n = 8), or a 60 min treadmill march at a fixed rate of heat production (∼500 W, n = 9). LSR of the forearm was concurrently measured with a VC and the KuduSmart® device secured within ∼2 cm. Using a ratio scale Bland–Altman analysis with the VC as the reference, the KuduSmart® device demonstrated systematic bias and not acceptable agreement for minute-averaged LSR (1.17 [1.09, 1.27], CV = 44.5%), systematic bias and acceptable agreement for steady-state LSR (1.16 [1.09,1.23], CV = 19.5%), no bias and acceptable agreement for thermosensitivity (1.07 [0.99, 1.16], CV = 23.2%), and no bias and good agreement for sweating onset (1.00 [1.00, 1.00], CV = 11.1%). In total, ≥73% of all minute-averaged LSR observations with the KuduSmart® device ( n = 2743) were within an absolute error of <0.2 mg/cm 2 /min to the VC, the reference minimum detectable change in measurement error of a VC on the forearm. Collectively, the KuduSmart® device may be a satisfactory solution for assessing the sweating response to heat stress where a VC is impractical.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".