Application of PainDETECT in pediatric chronic pain: how well does it identify neuropathic pain and its characteristics?
Bibliographic record
Abstract
Abstract Introduction: Neuropathic pain (NP) arises from nerve damage or disease, and when not defined, it can impair function and quality of life. Early detection allows for interventions that can enhance outcomes. Diagnosis of NP can be difficult if not properly evaluated. PainDETECT is a NP screening tool developed and successfully used in adults. Objectives: We evaluated the validity of painDETECT in a pediatric population. Methods: Adolescents and young adults (10–19 years old) completed painDETECT and quantitative sensory testing (QST), which assessed mechanical allodynia and hyperalgesia, common symptoms of NP. Pain diagnoses, including neuropathic pain (n = 10), were collected through documentation in the medical chart. Descriptive statistics were used to examine age, gender, pain diagnoses, and painDETECT scores. Kruskal–Wallis H tests were conducted to examine differences in QST results across painDETECT categorizations. Results: Youth with chronic pain (N = 110, Mage = 15.08 ± 2.4 years, Nfemale = 88) and peers without pain (N = 55, Mage = 15.84 ± 3.9 years, Nfemale = 39) completed the painDETECT. The painDETECT scores for youth with pain (M = 12.7 ± 6.76) were significantly higher than those for peers without pain (M = 2.05 ± 2.41). PainDETECT demonstrated 80% sensitivity and 33% specificity in a pediatric population. Individuals who screened positively on the PainDETECT had significantly higher mechanical allodynia (M = 0.640 ± 0.994) compared with those who screened negatively (M = 0.186 ± 0.499; P = 0.016). Conclusion: PainDETECT demonstrated the ability to screen for NP, and QST mechanical allodynia results were consistent with a positive NP screen. Results of the study offer preliminary support for the ongoing assessment of the painDETECT as a brief, inexpensive, and simple-to-use screening tool for pediatric patients with primary pain complaints.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".