Item Reduction and Scoring of the Pediatric PainSCAN©
Bibliographic record
Abstract
OBJECTIVES: The Pediatric PainSCAN© is the first screening tool specifically designed for pediatric neuropathic pain (NP) and complex regional pain syndrome (CRPS). A draft tool (37 items) was developed through a phased approach. This research aimed to reduce the number of items in the tool and to determine the weight of each item (ie, item scores) as it contributes to a probability of a diagnosis of NP or CRPS. METHODS: Survey 1 was administered to patients with NP or CRPS and health care professionals (HCPs). Participants rated the frequency and importance of each item in the draft tool on a 0 to 4 Likert scale; highest rated items were retained. Survey 2 was administered to a separate pool of patients (with NP, CRPS, or other pain conditions) who completed the Pediatric PainSCAN©. A logistic regression model was used to examine the relationship between item responses (yes/no) and patient diagnosis; parameter estimates were used for item scores. RESULTS: Survey 1 was completed by 43 patients (median age 15 y, 77% female) and 74 HCPs. Survey 2 was completed by 221 patients (median age 15 y, 81% female). Nineteen items remained in the final version of the tool; items were rated as important by patients and HCPs. Item scores are summed and converted to a probability score. CONCLUSION: The final set of items in the Pediatric PainSCAN© has been determined and study results provide further evidence of content validity. Additional research is needed to evaluate the tool's reliability, criterion validity, and select a cutoff score.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".