Nurse screening for neuropathic pain in postoperative patients
Bibliographic record
Abstract
This study was designed to audit nurse assessment and documentation for neuropathic pain in postoperative patients. The audit focused on recorded signs of neuropathic pain in the immediate postoperative period. Nurses were educated on how to screen patients for neuropathic signs using the validated and reliable 7-item DN4. Data were obtained from 450 patient charts from the thoracic, orthopaedic and spinal units. Of the 450 patient charts reviewed, 423 included a record of nurse screening of neuropathic pain signs. Screening by nurses found 24% (n=102) of the patients reported between one and four signs of neuropathic pain within the first 3 days following their surgery. This study demonstrated that the incorporation of the 7-item DN4 neuropathic pain assessment tool within the generic pain chart enabled nurses to regularly screen postoperative patients for signs of neuropathic pain in the immediate postoperative period.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".