Pain burden in children with cerebral palsy (CPPain) survey: Study protocol
Bibliographic record
Abstract
Pain is a significant health concern for children living with cerebral palsy (CP). There are no population-level or large-scale multi-national datasets using common measures characterizing pain experience and interference (ie, pain burden) and management practices for children with CP. The aim of the CPPain survey is to generate a comprehensive understanding of pain burden and current management of pain to change clinical practice in CP. The CPPain survey is a comprehensive cross-sectional study. Researchers plan to recruit approximately 1400 children with CP (primary participants) across several countries over 6-12 months using multimodal recruitment strategies. Data will be collected from parents or guardians of children with CP (0-17 years) and from children with CP (8-17 years) who are able to self-report. Siblings (12-17 years) will be invited to participate as controls. The CPPain survey consists of previously validated and study-specific questionnaires addressing demographic and diagnostic information, pain experience, pain management, pain interference, pain coping, activity and participation in everyday life, nutritional status, mental health, health-related quality of life, and the effect of the COVID-19 pandemic on pain and access to pain care. The survey will be distributed primarily online. Data will be analyzed using appropriate statistical methods for comparing groups. Stratification will be used to investigate subgroups, and analyses will be adjusted for appropriate sociodemographic variables. The Norwegian Regional Committee for Medical and Health Research Ethics and the Research Ethics Board at the University of Minnesota in USA have approved the study. Ethics approval in Canada, Sweden, and Finland is pending. In addition to dissemination through peer-reviewed journals and conferences, findings will be communicated through the CPPain Web site (www.sthf.no/cppain), Web sites directed toward users or clinicians, social media, special interest groups, stakeholder engagement activities, articles in user organization journals, and presentations in public media.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".