Age and Sex Differences in Pediatric Neuropathic Pain and Complex Regional Pain Syndrome
Bibliographic record
Abstract
BACKGROUND: Age and sex differences may exist in the frequency (incidence, prevalence) or symptoms of neuropathic pain (NP) and complex regional pain syndrome (CRPS) due to biopsychosocial factors (eg, neurodevelopment, physiological and hormonal changes, psychosocial differences) that evolve through childhood and adolescence. Age and sex differences may have implications for evaluating screening and diagnostic tools and treatment interventions. OBJECTIVE: To map the existing literature on pediatric NP and CRPS with respect to age and sex distributions, and age and sex differences in symptomology and frequency. METHODS: A scoping literature review was conducted. Databases were searched from inception to January 2023. Data were collected on study design, setting, demographics, and age and sex differences in frequency and symptoms. RESULTS: Eighty-seven studies were included. Distribution of participants with CRPS (n=37 studies) was predominantly early adolescence (10 to 14 y) and female sex, while NP (n=42 studies) was most commonly reported throughout adolescence (10 to 19 y) in both sexes. Forty-one studies examined age and sex differences in frequency; 6 studies reported higher frequency in adolescence. Very few studies (n=11) examined differences in symptomology. DISCUSSION: Large epidemiological studies are required to further understand age and sex differences in frequency of pediatric NP and CRPS. Age and sex differences must be considered when evaluating screening and diagnostic tools and treatment interventions to ensure relevance and validity to both sexes and across ages. Validated tools will improve understanding of age-dependent and sex-dependent differences in symptoms, pathophysiology, and psychosocial impact of pediatric NP and CRPS.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".