Reliability and validity of the Pediatric PainSCAN: a screening tool for pediatric neuropathic pain and complex regional pain syndrome
Bibliographic record
Abstract
ABSTRACT: The Pediatric PainSCAN is the first screening tool for neuropathic pain (NP) and complex regional pain syndrome (CRPS) designed for pediatrics. Prior research developed the tool and established content validity. The tool has 3 parts, part A is a preface (pain location, severity, duration), part B discriminates NP or CRPS from other pain conditions, and part C discriminates CRPS from NP. This study aimed to evaluate the tool's reliability and validity. A multicentre cross-sectional survey was administered to participants with NP, CRPS, and other pain conditions in pediatric chronic pain clinics. Test-retest reliability was evaluated by readministering the tool after 7 days. Criterion validity (sensitivity [SE] and specificity [SP]) was evaluated by comparing participant scores to a clinician diagnosis. Convergent validity was evaluated by comparing participant scores on the tool to existing NP screening tools. Participants (N = 221; 56 with NP, 57 with CRPS, 108 with other pain) were aged 9 to 18 years and 81% female. Test-retest reliability (intraclass correlation coefficients part B = 0.76 and part C = 0.82) was sufficient (>0.70). Criterion validity (part B: SE 76%, SP 63%; part C: SE 83%, SP 77%) was sufficient (>70%) except for SP of part B. Convergent validity was sufficient (correlation coefficients aligned with hypotheses: painDETECT [0.73], self-report Leeds Assessment of Neuropathic Symptoms and Signs [0.73], and Patient-Reported Outcome Measurement Information System Neuropathic Pain Quality [0.59]). The Pediatric PainSCAN demonstrated sufficient reliability and validity to screen for NP and CRPS in pediatric chronic pain clinics. Future research is needed to evaluate the tool in other settings and determine the utility of implementing the tool in clinical practice.
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 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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".