Development of a Screening Tool for Pediatric Neuropathic Pain and Complex Regional Pain Syndrome
Bibliographic record
Abstract
OBJECTIVE: Neuropathic pain (NP) and complex regional pain syndrome (CRPS) in children can result in significant disability and emotional distress. Early assessment and treatment could potentially improve pain, function, quality of life, and reduce costs to the health care system. Currently, there are no screening tools for pediatric NP and CRPS. This research aimed to develop and establish the content validity of a screening tool for pediatric NP and CRPS using a phased approach. MATERIALS AND METHODS: Phase I surveyed clinical experts using a modified Delphi procedure to elicit disease concepts for inclusion. In phase II, a consensus conference including clinicians, researchers, and people with lived experience, informed the initial item pool. Consensus for item inclusion was achieved using a nominal group technique for voting. Phase III used iterative rounds of cognitive interviews with children aged 8 to 18 years with CRPS or NP to evaluate the tool's comprehensiveness and individual item relevance and comprehensibility. Descriptive statistics were used to describe participant characteristics. Content analysis was used to analyze patient interviews. RESULTS: Phase I (n=50) generated an initial item pool (22 items). Phase II generated a comprehensive item pool (50 items), after which an initial version of the screening tool was drafted. Following phase III (n=26) after item revision and elimination, 37 items remained. DISCUSSION: The Pediatric PainSCAN is a novel screening tool that has undergone rigorous development and content validity testing. Further research is needed to conduct item reduction, determine scoring, and test additional measurement properties.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".