Acute Neuropathic Pain Assessment in Burn Injured Patients
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to measure the prevalence of acute neuropathic pain in patients with acute burn injuries and the demographic and clinical characteristics of neuropathic pain in this population. We also evaluated the proportion of patients who received twice-daily evaluation of nurses' documentation of neuropathic pain following introduction of a validated neuropathic pain assessment tool embedded within the pain chart. DESIGN: Retrospective, descriptive study. SUBJECTS AND SETTING: The sample comprised 86 patients with second- and third-degree burn injuries. The research setting was a burn injury unit in a provincial center in British Columbia, Canada. METHODS: Medical records over a 1-year prior following introduction of assessment of neuropathic pain into pain charts were retrospectively reviewed, and data collection focused on evidence of nurses undertaking acute neuropathic pain assessment as well as prevalence of report of acute neuropathic pain signs among this patient group. Neuropathic pain was evaluated twice daily using the Douleur Neuropathique 4, a previously validated neuropathic pain assessment tool. RESULTS: Eighty percent of patients cared for received twice-daily neuropathic pain assessment. The prevalence of patients with neuropathic pain based on the Douleur Neuropathique instrument scores was 42%. Males reported neuropathic signs more than female patients, and patients with a greater than 10% body surface burn had a higher prevalence of neuropathic pain. CONCLUSION: Study findings suggest that patients with acute burn injury are at risk of neuropathic pain. We recommend that nurse assessment of neuropathic pain becomes routine during the acute injury phase.
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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 | 0.000 | 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".