A Novel Model to Predict Cutaneous Finger Blood Flow via Finger and Rectal Temperatures
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Bibliographic record
Abstract
OBJECTIVES: To generate a model that predicts fingertip blood flow (BF(f) ) and to cross-validate it in another group of subjects. METHODS: We used fingertip temperature (T(f)), forearm temperature minus T(f) (T(For-f)), rectal temperature (T(re)), and their changes across time ((lag) T) to estimate BF(f). Ten participants (six male, four female) were randomly divided into "model" and "validation" groups. We employed a passive hot-cold water immersion protocol during which each participant's core temperature increased and decreased by 0.5°C above/below baseline during hot/cold conditions, respectively. A hierarchical multiple linear regression analysis was introduced to generate models using temperature indicators and (lag) T (independent variables) obtained from the model group to predict BF(f) (dependent variable). RESULTS: Mean BF(f) (109.5 ± 158.2 PU) and predicted BF(f) (P-BF(f)) (111.4 ± 136.7 PU) in the model group calculated using the strongest (R(2) = 0.766, p < 0.001) prediction model [P-BF(f) =T(f) × 19.930 + (lag4) T(f) × 74.766 + (lag4) T(re) × 124.255 - 447.474] were similar (p = 0.6) and correlated (r = 0.880, p < 0.001). Autoregressive integrated moving average time-series analyses demonstrated a significant association between P-BF(f) and BF(f) (R(2) = 0.381; Ljung-Box statistic = 8.097; p < 0.001) in the validation group. CONCLUSIONS: We provide a model that predicts BF(f) via two practical temperature indicators that can be implemented in both clinical and field settings.
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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 | 0.000 |
| 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 it