Comparing the DN4 tool with the IASP grading system for chronic neuropathic pain screening after breast tumor resection with and without paravertebral blocks
Bibliographic record
Abstract
Investigating protective strategies against chronic neuropathic pain (CNP) after breast cancer surgery entails using valid screening tools. The DN4 (Douleur Neuropathique en 4 questions) is 1 tool that offers important research advantages. This prospective 6-month follow-up study seeks to validate the DN4 and assess its responsiveness in screening for CNP that satisfies the International Association for the Study of Pain (IASP) definition and fulfills its grading system criteria after breast tumor resection with and without paravertebral blocks (PVBs). We randomized 66 females to standardized general anesthesia and sham subcutaneous injections, or PVB and total intravenous anesthesia. The 6-month CNP risk was assessed using the IASP grading system and the DN4 screening tools. We evaluated the DN4 sensitivity, specificity, and responsiveness in capturing the impact of PVB on the CNP risk relative to the IASP grading system. Data from 64 patients showed similar demographic characteristics in both groups. Twenty patients in both groups met the grading system CNP criteria; among these, 18 patients also met the DN4 CNP criteria. Furthermore, 15 patients in both groups did not meet the grading system CNP criteria; among these, 9 patients also did not meet the DN4 CNP criteria. Therefore, the sensitivity and specificity of the DN4 were estimated at 90% and 60%, respectively. Both screening tools suggested that PVB reduced the 6-month CNP risk. Our results suggest that the DN4 can reliably identify CNP at 6 months after breast tumor resection and detect the preincisional PVB effect on the risk of developing such pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".