Assessment of Altmetrics and PlumX Metrics Scoring as Mechanisms to Evaluate the Top 100 Trending Hidradenitis Suppurativa Articles on Social Media: Cross-Sectional Study
Bibliographic record
Abstract
Background Dermatologists are increasingly utilizing social media platforms to disseminate scientific information. New tools, such as altmetrics and PlumX metrics, have been made available to rapidly capture the level of scientific article dissemination across social media platforms. However, no studies have been performed to assess the level of scientific article dissemination across social media regarding hidradenitis suppurativa, a disease that is still currently not well understood. Objective The aim of our study was to evaluate the utility of altmetrics and PlumX metrics by characterizing the top 100 “trending” hidradenitis suppurativa articles in the altmetric database by the altmetric attention score and PlumX score. Methods Altmetric data components of the top 100 hidradenitis suppurativa articles were extracted from the altmetric database. Article citation count was found using Web of Science. PlumX field-weighted impact scores for each article were collected from the Scopus database. Journal title, open-access status, article type, and study design of original articles were assessed. Additionally, the altmetric attention score, PlumX score, and citation count were log transformed and adjusted by +1 for linear regression, and Spearman correlation coefficients were utilized to determine correlations. Results Most of the top 100 “trending” hidradenitis suppurativa articles were published in JAMA Dermatology (n=27, 27%). The median altmetric attention score, PlumX score, and citation count were 25.5, 3.7, and 10.5, respectively. The most mentions regarding social media platforms came from Twitter. Although no correlation was observed between the citation count and altmetric attention score (r2=0.019, P=.17), positive correlation was observed between the citation count and PlumX score (r2=0.469, P<.001). Conclusions Our research demonstrated that citation count is not correlated with the altmetric attention score, but is strongly correlated with the PlumX score regarding hidradenitis suppurativa articles at this point in time. With the continual increase of social media usage by medical professionals and researchers, this study can help investigators understand the best way to captivate their audience.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".