A review and bibliometric analysis of global research on non-pharmacologic management for neonatal and infant procedural pain
Bibliographic record
Abstract
Repeated and prolonged exposure to pain can impair neurodevelopmental, behavioral, and cognitive outcomes in newborns. Effective pain management of newborns is essential, but there is no comprehensive analysis of the status of neonatal pain non-pharmacologic management research. Original publications related to the non-pharmacological management of neonatal pain were obtained from the Web of Science Core Collection (WOSCC) between 1989 and 2024. CiteSpace and VOSviewer were used to extract information about countries/regions, institutions, authors, keywords, and references to identify and analyze the research hotspots and trends in this field. 1331 authors from 51 countries and 548 institutions published studies on the non-pharmacological management of neonatal pain between 1989 and 2024, with the number of publications showing an overall upward trend. Canada emerged as the leading country in terms of publication volume, with the University of Toronto and The Hospital for Sick Children identified as key research institutions. High-frequency keywords included "procedural pain," "management," "sucrose," "analgesia," and "preterm infant," resulting in 11 clusters. Keyword emergence analysis revealed that "neonatal pain," "analgesia," "oral sucrose," and "oral glucose" were research hotpots. Analysis of highly cited papers showed that the most referenced articles were published in the Clinical Journal of Pain. Researchers' interest in neonatal procedural pain has increased significantly over the past 30 years. This article can serve as a theoretical reference for future research on mild to moderate pain in neonates and infants, and it can provide ideas for exploring novel and secure pain management strategies.
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
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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.148 | 0.201 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".