Immersion in Cold-Water Evaluation (ICE) and Self-reported Cold Intolerance are Reliable but Unrelated Measures
Bibliographic record
Abstract
Intolerance to the cold is common following peripheral nerve injury and surgery of the upper extremity. Despite its prevalence, the exact pathophysiology and natural history of this condition are not well understood. Subjective, self-report questionnaires have been created and validated as reliable measures of post-traumatic cold intolerance. The difficulty currently lies in assigning an objective measure to this predominantly subjective phenomenon. The present study evaluated the test-retest reliability of a proposed objective measure of cold intolerance, the Immersion in Cold-water Evaluation (ICE), and its correlation with subjective measures in healthy control subjects. Two age groups were also compared to investigate the effect of age on cold intolerance and temperature recovery. On two separate testing days, subjects completed three health-related questionnaires and submersed their dominant hands in cold water. The temperature of their second and fifth digits was monitored during recovery. Both the objective cold-provocation testing and the subjective self-report questionnaires were highly reliable albeit not significantly correlated. No significant temperature recovery trend was noted between the age groups. Post-traumatic cold intolerance is postulated to have both a vascular and neural etiology among other contributing causes. The protocol studied here was centered predominantly on the former etiology, examining peripheral blood flow and associated temperature recovery. This study established ICE as a reliable means to objectively measure cold response, supplementing information provided by previously validated self-report methods.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".