An Analysis of Altmetrics in Emergency Medicine
Bibliographic record
Abstract
OBJECTIVES: Alternative-level metrics (Altmetrics) are a new method to assess the sharing and spread of scientific knowledge. The primary objective of this study was to describe the traditional metrics and Altmetric scores of the 50 most frequently cited articles published in emergency medicine (EM) journals. Since many articles related to EM are published in other journals, the secondary aim of this study was to describe the Altmetric scores of the most frequently cited articles relevant to EM in other biomedical journals. METHODS: A structured search of the Institute for Scientific Information Web of Science version of the Science Citation Index Expanded was conducted. The 200 most frequently cited articles in the top 10 EM journals (2011 Journal Citation Report) were identified. The 200 most frequently cited articles from the rest of the medical literature, matching a predefined list of keywords relevant to the specialty of EM, were identified. Two authors reviewed the lists of citations for relevance to EM and a consensus approach was used to arrive at the final lists of the top 50 cited articles. The Altmetric scores for the top 50 cited articles in EM and other journals were determined. Descriptive statistics and Spearman correlation were performed. RESULTS: The highest Altmetric score for EM articles was 25.0; the mean (±SD) was 1.9 (±5.0). The EM journal with the highest mean article Altmetric score was Resuscitation. The main clinical areas shared for articles from EM articles were trauma (mean ± SD = 11.0 ± 15.6, median = 11.0) and cardiac arrest (mean ± SD = 2.7 ± 5.8, median = 0). The highest Altmetric score for other journals was 176.0 (mean ± SD = 23.3 ± 40.8). The other journal with the highest mean article Altmetric score was the New England Journal of Medicine. The main clinical areas shared for articles were critical care (mean ± SD score = 36.5 ± 47.4, median = 36.5), sepsis (mean ± SD = 24.6 ± 48.8, median = 12.0), cardiology (mean ± SD = 19.2 ± 35.6, median = 7.0), and infectious diseases (mean ± SD = 17.0 ± 12.7, median = 17.0). Spearman correlation demonstrated weakly positive correlation between citation counts and Altmetric scores for EM articles and other journals. CONCLUSIONS: This study is the first analysis of Altmetric scores for the top cited articles in EM. We demonstrated that there is a mild correlation between citation counts and Altmetric scores for the top papers in EM and other biomedical journals. We also demonstrated that there is a gap between the sharing of the top articles in EM journals and those related to EM in other biomedical journals. Future research to explore this relationship and its temporal trends will benefit the understanding of the reach and dissemination of EM research within the scientific community and society in general.
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 | 0.017 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.030 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".