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 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 | 0.012 | 0.008 |
| 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".