A comprehensive categorical and bibliometric analysis of published research articles on pediatric pain from 1975 to 2010
Bibliographic record
Abstract
The field of pediatric pain research began in the mid-1970s and has undergone significant growth and development in recent years as evidenced by the variety of books, conferences, and journals on the topic and also the number of disciplines engaged in work in this area. Using categorical and bibliometric meta-trend analysis, this study offers a synthesis of research on pediatric pain published between 1975 and 2010 in peer-reviewed journals. Abstracts from 4256 articles, retrieved from Web of Science, were coded across 4 categories: article type, article topic, type and age of participants, and pain stimulus. The affiliation of the first author and number of citations were also gathered. The results suggest a significant increase in the number of publications over the time period investigated, with 96% of the included articles published since 1990 and most research being multiauthored publications in pain-focused journals. First authors were most often from the United States and affiliated with a medical department. Most studies were original research articles; the most frequent topics were pain characterization (39.86%), pain intervention (37.49%), and pain assessment (25.00%). Clinical samples were most frequent, with participants most often characterized as children (6-12 years) or adolescents (13-18 years) experiencing chronic or acute pain. The findings provide a comprehensive overview of contributions in the field of pediatric pain research over 35 years and offers recommendations for future research in the area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.242 | 0.265 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".