Evaluation of Cold Sensitivity, Pain, and Quality of Life After Upper Extremity Nerve Injury
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to evaluate the relationship between reported cold sensitivity, pain, and impact on quality of life (QoL) after upper extremity nerve injury. METHODS: This cross-sectional study included adults more than 6 months after an upper extremity nerve injury. Assessment included the Pain Evaluation Questionnaire (pain descriptors, questionnaire, pain intensity, impact on QoL), and Cold Intolerance Severity Scale (CISS). Statistical analyses evaluated the relationships between the Pain Evaluation Questionnaire, CISS, and independent variables. RESULTS: There were 70 patients (mean age 42 ± 16 years). There were high levels of pain, cold sensitivity, and impact on QoL reported. Patients selecting the adjective "coldness" had significantly higher CISS scores (P = .005), pain intensity (P= .008), and impact on QoL (P < .006). Impact on QoL and CISS (r = .35) were moderately correlated. There were significant correlations (P < .01) between the level of cold-induced pain and CISS (r = .78), overall pain intensity (r = .58), pain descriptor score (r = .49), and impact on QoL (r = .32). CONCLUSIONS: Cold-induced pain is associated with higher cold sensitivity scores and greater impact on QoL. Reporting a single descriptor "coldness" and ranking cold-induced symptoms were strongly associated with higher cold sensitivity scores and impact on health-related QoL. This may have important implications for quick screening to identify patients with cold sensitivity, and future studies in larger patient samples are necessary to provide additional evidence.
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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 | 0.003 |
| 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) | 0.002 | 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".