Visualización y temas emergentes de la producción científica global sobre el síndrome de abstinencia neonatal: una aproximación bibliométrica
Bibliographic record
Abstract
El síndrome de abstinencia neonatal (SAN) afecta a los recién nacidos expuestos a opioides o sustancias adictivas durante la gestación. Esta afección ha mostrado un incremento en las últimas décadas, especialmente en países de altos ingresos. El objetivo de este estudio fue identificar las características bibliométricas y visualizar los temas emergentes en la producción científica global sobre el SAN. Se realizó un análisis bibliométrico de documentos recuperados de la base de datos SCOPUS entre 1994 y 2023. Se empleó una estrategia de búsqueda con términos del Medical Subject Headings (MeSH), Emtree y términos libres. Se analizaron el crecimiento anual, las redes de colaboración, las palabras clave más frecuentes y los artículos más citados. Se captaron 1,455 documentos, con un crecimiento anual del 9.57% y un coeficiente de determinación de 0.89. El 37.59% de los estudios se encuentran en acceso abierto. EE.UU. lideró la producción científica con un 52.4% de los documentos, seguido de Canadá (4.9%) y Australia (3.9%). Las palabras clave más frecuentes después de 2020 fueron "sleep", "neonatal opioid withdrawal syndrome" y "neurodevelopment". La producción científica sobre el SAN ha aumentado considerablemente en las últimas décadas, con preponderancia de estudios en EE.UU. y Canadá. Los futuros estudios deberían enfocarse en el diagnóstico, tratamiento y la carga de incidencia y prevalencia en países de ingresos bajos y medianos. Neonatal abstinence syndrome (NAS) is a condition that affects newborns exposed to opioids or addictive substances during gestation. The prevalence of this condition has increased significantly in recent decades, particularly in high-income countries. This study aimed to identify the bibliometric characteristics and visualize emerging topics in the global scientific production on NAS. A bibliometric analysis was conducted using documents retrieved from the SCOPUS database between 1994 and 2023. The search strategy incorporated terms from Medical Subject Headings (MeSH), Emtree, and free-text keywords. Annual growth, collaboration networks, the most frequent keywords, and the most cited articles were analyzed. A total of 1,455 documents were retrieved, with an annual growth rate of 9.57% and a coefficient of determination of 0.89. Open-access publications accounted for 37.59% of the studies. The United States led the scientific production with 52.4% of the documents, followed by Canada (4.9%) and Australia (3.9%). The most frequent keywords after 2020 were “sleep,” “neonatal opioid withdrawal syndrome,” and “neurodevelopment.” Scientific production on NAS has increased substantially over the past decades, with a predominance of studies conducted in the United States and Canada. Future research should focus on the diagnosis, treatment, and burden of incidence and prevalence in low- and middle-income countries.
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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 | 0.022 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.093 | 0.140 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".