Burn-related chronic pain: quantitative and qualitative characteristics and its impact on health-related quality of life
Bibliographic record
Abstract
Inadequate burn-related pain control contributes to poor functional and psychological outcomes, has directly impact on patients recovery and quality of life (QL). Burn-related pain is one of the most severe forms of pain experienced by patients. The aim of study: To investigate the frequency of chronic pain, the quantitative and qualitative assess of their performance, quality of life patients with burns after 6 months period. Methods: A cross sectional study carried out in Kaunas medical hospital in Lithuania. Standard McGill Pain Questionnaire (Lithuanian version), numeral analog pain intensity measuring scale (NAS) and health-related quality of life (SF-36) Q were sent by email to burn victims treated in a single burn unit during period og 01/06/2008-30/04/2009. Results: Response rate - 36.7%. The mean age of respondents - 51.24±16.68.2% of patients indicated that they are suffering from varied intensity of pain in burnt place, 51.6% - felt severe pain in skin donoric site. Patients, experienced wound infection in acute trauma period, indicates higher NAS score (p=0.03). 86.3% of our patients were generally noted the emotional scale: one quarter - fearful and one-third-tiring, 84.38% were marked neuropathic pain scales: not burning - 36.4%, growing - 29.5%, tingling - 20.5% (p=0.01). [...].
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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 | 0.004 |
| 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 | 0.001 |
| 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".