Publication Trends in Neurology, the Official Journal of the American Academy of Neurology (P2.395)
Bibliographic record
Abstract
Objective: To determine temporal trends in articles published in the journal Neurology in recognition of the 65th anniversary of the journal in 2016. Background: The first issue of Neurology was published in January 1951, with Russell DeJong serving as founding editor. As former editor-in-chief Robert Griggs wrote fifty years later, “the growth of Neurology has more than matched the expansion of clinical neuroscience.” By reviewing a representative sample of articles published, we trace the development of Neurology as it has become the most widely read and highly cited peer-reviewed neurology journal. Design/Methods: We reviewed abstracts or full-text of articles (in cases where an abstract was not published or further information was required) beginning with the first volume published in 1951 followed by subsequent volumes in 5-year increments. Articles were analyzed for authorship, disorders of interest, diagnostic methods, number of patients enrolled/described, and type of study (basic science or clinical). Results: 2161 articles were included in this study. Increasing internationalization of authors, rising from 4[percnt] to 55[percnt] non-United States authors from 1951 to 2011, along with increasing number of authors per article, was observed. The median number of patients enrolled/described in articles increased from 12.5 in 1951 to 100.5 in 2011. Across the period of review, an increasing proportion of articles were related to neurodegenerative disorders and multiple sclerosis, while a decreasing proportion of articles were related to infectious diseases and neoplastic and structural disorders. The utilization of new diagnostic methods (e.g. genetic studies and functional and cross-sectional imaging) was reflected in articles at specific time points. Conclusions: Temporal trends revealed in this study reflect changes in clinical practice and research interests and an increased need for higher quality evidence. This study allows neurologists and neuroscientists to critically reflect on current research interests and publication practices in the context of historical data.
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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 | 0.014 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".