Myths and methodologies: Reliability of forearm cutaneous vasodilatation measured using laser‐Doppler flowmetry during whole‐body passive heating
Bibliographic record
Abstract
Abstract Laser‐Doppler flowmetry (LDF) is commonly used to assess cutaneous vasodilatation responses, but its reliability (i.e. consistency) during whole‐body passive heating is unknown. We therefore assessed the reliability of LDF‐derived indices of cutaneous vasodilatation during incremental whole‐body heating. Fourteen young men (age: 24 (SD 5) years) completed three identical trials, each separated by 1 week. During each trial, a water‐perfused suit was used to raise and clamp oesophageal temperature at 0.6°C (low‐heat strain; LHS) and 1.2°C (moderate‐heat strain; MHS) above baseline. LDF‐derived skin blood flow (SkBF) was measured at three dorsal mid‐forearm sites, with local skin temperature clamped at 34°C. Data were expressed as absolute cutaneous vascular conductance (CVC abs ; SkBF/mean arterial pressure) and normalised to maximal conductance (%CVC max ) achieved via simultaneous local skin heating to 44°C and increasing oesophageal temperature to 1.8°C above baseline. Between‐day reliability was characterised as measurement consistency across trials, while within‐day reliability was characterised as measurement consistency across adjacent skin sites during each trial. Between‐ and within‐day absolute reliability (coefficient of variation) generally improved with increasing heat strain, changing from poor (>25%) at baseline, poor‐to‐moderate (15–34%) at LHS, and moderate (10–25%) at MHS. Generally, these estimates were more consistent when expressed as %CVC max . Conversely, relative reliability was mostly acceptable (intraclass correlation coefficient ≥0.70) during LHS and when data were expressed as CVC abs . These findings indicate that the consistency of LDF‐derived CVC estimates during heat stress depends on the level of heat strain and method of data expression, which should be considered when designing and interpreting experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".