Smartphone use in Neurology: a bibliometric analysis and visualization of things to come
Bibliographic record
Abstract
Background and objectives: Smartphones are a ubiquitous part of society with increasing use as a healthcare tool. We aimed to analyze the published literature on smartphone usage within the field of Neurology to define the scientific landscape and forecast future research initiatives. Methods: We performed a bibliometric review of smartphone uses in Neurology based on a search of two Web of Science databases from inception through September 16, 2022. This librarian-guided review was conducted using Bibliometrix for data assessment and visualization. Temporal trends in publications, citation counts, collaborations, and author affiliations were among key metrics evaluated. VOS viewer identified hot spots based on generating co-occurrences and bibliographic coupling mapping. Results: were the most cited journals. Rapid growth in scientific production occurred in recent years, including during the COVID-19 pandemic. Hotspots and keyword co-occurrence included telehealth, machine learning, and self-management. Temporal trends reflect transitioning from a focus of initial publications regarding mobile phone safety to more recent application of smartphones as "smart" tools for single modality diagnosis, monitoring, management, and treatment of neurological diseases. Discussion: There has been rapid expansion of the published literature on smartphone uses in Neurology. Initial focus on smartphones and health risk has shifted to uses for neurological disease diagnosis, detection, and management, with relevance as a global interface for collaboration and clinical practice.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.192 | 0.208 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".