Prevalence of Cold Sensitivity in Patients with Hand Pathology
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to evaluate the prevalence of cold sensitivity in patients with hand- and wrist-related diagnoses. METHODS: We included English-speaking adults who were more than 1 month following hand injury or onset of symptoms. Patients were asked if exposure to cold air or water provoked cold-related symptoms and to rank symptom severity (scale 0-10). Statistical analyses evaluated the relationships between the cold sensitivity and independent variables (age, gender, history of trauma, and time from injury/symptoms). RESULTS: There were 197 patients (mean age 49 ± 16 years): 98 trauma and 99 non-trauma cases. Cold-induced symptoms were reported by 34 %, with 10 % reporting severe symptoms. Exposure to cold air is the most common catalyst; mean severity score was 6.7 ± 2.2. Those with traumatic injuries compared to non-trauma diagnoses reported significantly more cold-induced symptoms (p = .04). Using backward linear regression, the significant predictors of cold symptom severity were trauma (p = .004) and time since onset (p = .003). Including only the trauma patients in the regression model, the significant predictor was time since injury (p = .005). CONCLUSIONS: Cold-induced symptoms are reported by more than 30 % of hand-related diagnoses, and exposure to cold air was the most commonly reported trigger. The significant predictors of cold-induced symptoms are traumatic injuries and longer time from injury. This study provides evidence of the common problem of cold sensitivity in patients with hand pathology. LEVEL OF EVIDENCE: Prognostic Level II.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".