A Novel Model to Predict Cutaneous Finger Blood Flow via Finger and Rectal Temperatures
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".