Modified iodine-paper technique for the standardized determination of sweat gland activation
Bibliographic record
Abstract
Quantifying sweat gland activation provides important information when explaining differences in sweat rate between populations and physiological conditions. However, no standard technique has been proposed to measure sweat gland activation, while the reliability of sweat gland activation measurements is unknown. We examined the interrater and internal reliability of the modified-iodine paper technique, as well as compared computer-aided analysis to manual counts of sweat gland activation. Iodine-impregnated paper was pressed against the skin of 35 participants in whom sweating was elicited by exercise in the heat or infusion of methylcholine. The number of active glands was subsequently determined by computer-aided analysis. In total, 382 measurements were used to evaluate: 1) agreement between computer analysis and manual counts; 2) the interrater reliability of computer analysis between independent investigators; and 3) the internal reliability of sweat gland activation measurements between duplicate samples. The number of glands identified with computer analysis did not differ from manual counts (68 ± 29 vs. 72 ± 24 glands/cm(2); P = 0.27). These measures were highly correlated (r = 0.77) with a mean bias ± limits of agreement of -4 ± 38 glands/cm(2). When comparing computer analysis measures between investigators, values were highly correlated (r = 0.95; P < 0.001) and the mean bias ± limits of agreement was 4 ± 18 glands/cm(2). Finally, duplicate measures of sweat gland activation were highly correlated (r = 0.88; P < 0.001) with a mean bias ± limits of agreement of 3 ± 29 glands/cm(2). These results favor the use of the modified-iodine paper technique with computer-aided analysis as a standard technique to reliably evaluate the number of active sweat glands.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".