A Bibliometric Analysis of Publication Patterns in Pediatric Neurology
Bibliographic record
Abstract
Purpose To examine the publication patterns of pediatric neurology articles in general pediatric, general neurology, and neurology subspecialty journals using a bibliometric approach. Methods The top 5 journals in general pediatrics, general neurology and neurology subspecialties were identified using the 2017 Journal Citations Report (JCR). For general pediatric journals, we selected 4 pediatric subspecialties for comparison of publication patterns with neurology: immunology, endocrinology, gastroenterology, and respirology. For general neurology and neurology subspecialty journals, we searched both the top 5 neurology and neurology subspecialty journals for pediatric articles. Using Ovid Medline, we identified articles published between 2009–2017. Results With regards to child neurology-based articles, 1501 were published in general pediatrics journals, 643 in general neurology journals and 685 in neurology subspecialty journals. Examination of the top pediatric journals revealed that Pediatrics published the most neurology-based articles. Neurology-based studies were published more frequently than other pediatric subspecialty studies. Of the top general neurology Neurology published the most child neurology-based articles, while Epilepsia published the most child neurology-based articles out of neurology subspecialty journals. Cohort studies were the most frequent study type across all journals. Conclusion Our study revealed that child neurology articles are published more often in pediatric journals as opposed to general neurology and neurology subspecialty journals. We also found that in general pediatric journals, neurology-based articles are published more frequently compared to other specialties. Our results provide guidance to authors when considering submission of their pediatric neurology research.
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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 | 0.012 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.151 | 0.196 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".