WCN25-2064 ASSESSING THE IMPACT OF TWEETORIALS (XTORIALS) ON KNOWLEDGE DISSEMINATION AND ENGAGEMENT IN NEPHROLOGY
Bibliographic record
Abstract
Introduction: The Video Abstracts Series is an initiative by theISNEducation Working Group, in collaboration with theISNEducation Social Media Team, tosupporttheISNglobal education strategy, which was initiated in Nov 2021.Video abstracts serve as a cutting-edge tool for science communication, offering a concise overview of a scientific paper.They highlight the key aspects and findings of the study, within a maximum duration of 2 minutes and 20 seconds.The goal is to highlight the key aspects and findings of research, enhancing accessibility and engagement to a global audience.Methods: This study aimed to quantitatively evaluate the impact of the ISN Video Abstracts Series initiative.Data on video impressions, engagements, and views were collected and analyzed from platforms such as Twitter X, Facebook, LinkedIn, and Instagram, covering the period from November 2021 to August 2024.Results: Since November 2021, the ISN Video Abstracts Series has featured 68 studies published in Kidney International Reports (KIR, n¼33), Kidney International (KI, n¼34), and the ISN-DOPPS initiative (n¼1).Analysis of 56 videos revealed a total of 239,448 impressions, 7,380 engagements, and 47,929 video views.Additionally, 851 clicks redirected viewers to the original journal publications.The video abstract featuring the KIR publication "Nicotinamide Adenine Dinucleotide Biosynthetic Impairment and Urinary Metabolomic Alterations Observed in Hospitalized Adults With COVID-19-Related AKI" resulted in the highest views (n¼2,125) and impressions (n¼17,527).Conclusions: In conclusion, videos abstracts have proved to be a powerful tool in science communication, offering a dynamic and engaging way to disseminate research findings to a broader audience.By breaking down complex concepts into accessible visual narratives, visual abstracts enhance visibility and impact of research, fostering greater participation.As the demand for quick, easily digestible content continues to rise, video abstracts are well positioned to significantly amplify the reach and impact of research, serving as bridge between researchers and the global community.I have no potential conflict of interest to disclose.I did not use generative AI and AI-assisted technologies in the writing process.
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 | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".