Cryotherapy Effects, Part 1: Comparison of Skin Temperatures and Patient-Reported Sensations for Different Modes of Administration
Bibliographic record
Abstract
Context: Alterations in skin sensations may be responsible for pain reduction provided by cryotherapy, but the exact physiological mechanism is unknown. Objective: To investigate perceptions of skin sensations associated with different modes of cryotherapy administration and skin temperature at the point of perceived numbness. Design: Repeated measures. Participants: 30 healthy subjects (12 Male, 18 Female, Age = 21.1±1.9 years). Interventions: Crushed ice bag, ice massage, and cold water immersion. Main Outcome Measures: Perceptions of sensations during each mode of cryotherapy administration were derived from a Modified McGill Pain Questionnaire. Skin temperature was recorded when numbness was reported for each treatment. Results: Participants experienced sensations that included cold, tight, tingling, stinging, and numb. Ice massage sensations transitioned rapidly from cold to numb, whereas cold water immersion and ice bag treatments produced altered sensations for longer duration. Ice massage decreased skin temperature significantly more than the other two modes of cryotherapy administration. Conclusions: Ice massage may be the best mode of cryotherapy administration for achievement of anaesthesia as rapidly as possible, whereas cold water immersion and ice bag application may be better for attainment of pain reduction associated with noxious stimulation of skin receptors.
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 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 | 0.001 |
| 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) | 0.003 | 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".