A Bibliometric Analysis of Publications on the Prevalence of Chronic Pain in Children and Adolescents From 2009 to 2023
Bibliographic record
Abstract
ABSTRACT Bibliometric reviews explore patterns in publications in a given research area by exploring trends over time and the contributions by citations, such as relationships between authors and publications. Despite “chronic pain” being the second most common keyword in pain research, no bibliometric reviews have focused on publication trends related to the prevalence of chronic pain in children and adolescents. A bibliometric analysis was conducted with articles included in a systematic review and meta‐analysis on the prevalence of pediatric chronic pain to identify the recent trajectory of the field and guide future directions. Publication bibliometrics data from the articles were extracted and analyzed (e.g., gender of authors, citation counts, and countries) and was visualized in VOSViewer. Among 119 studies, the number of publications per year ranged from 4 (2023) to 11 (2014, 2021) with an average of 8/year. Articles were cited on average 36.6 times (SD = 51.7, range 0–380) with 5058 unique citations. There were 74 different journals represented, with most publishing only 1 article (n = 52, 70%). Seventy countries were represented in prevalence data, 78% from high‐income countries; fifteen (21.4%) had only one data point, primarily from low‐ and lower‐middle income countries. There were 109 different corresponding authors, with only 1 corresponding author who had more than 2 published articles. There was relative gender equity in terms of first and corresponding author. There was little to no collaboration between author groups identified. Despite a steady number of articles published over the 14‐year period, the literature on the prevalence of pediatric chronic pain appears fragmented with articles published in a wide variety of journals. Prevalence data from low‐ and lower‐middle‐income countries were under‐represented. Future work should focus on expanding evidence in underrepresented areas and greater collaboration among research groups to collect prevalence data in geographical areas where data gaps exist.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.162 | 0.199 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".