Bibliographic record
Abstract
Background: Rheumatological diseases have a strong impact on the lives of those affected. They have a clinical impact due to often challenging symptoms, but also psychological and social effects because they are chronic, systemic diseases. Additionally, the therapies can have side effects, and the diagnostic pathway can sometimes take a long time, accompanied by the uncertainty that comes with not having a diagnosis. Fortunately, in recent years, research has better understood the mechanisms underlying numerous rheumatological diseases, identified the role of inflammation and therapeutic targets, and developed innovative drug classes that can block the inflammatory cascade and lead to remission, if not cure. The goal is to prevent disease progression and disability. This new scenario makes it necessary to recount the changes in the treatment and diagnostic paradigm and, therefore, also to revise the language used to talk about rheumatological diseases. The language must adapt to innovation and emerging needs and to the increased awareness of patients, who today must be more active participants in their treatment pathway. Objectives: The vocabulary of rheumatology is and educational project promotes by the APMARR National Association of People with Reumatological and Rare Diseases to help patients navigate the wealth of information found on the web. It provides them with the tools to become protagonists of their own care pathway, exploring the terms that accompany the world of rheumatology. These terms come from the scientific sphere, including those related to drugs and biomedical research, but also from the emotional, psychological, cultural, and social spheres. Over the years, these terms have become cornerstones of care, such as "therapeutic alliance", "multidisciplinary approach", "adaptation", and "compliance". This project is aimed at patients and caregivers, with the goal of familiarizing them with the lexicon of rheumatology and making the concept of empowerment and engagement tangible. Methods: The Vocabulary of Rheumatology started in 2023 with a survey administered to the APMARR community (members, patients, caregivers) aimed at identifying the word that every person with rheumatological disease and their caregiver should know. The project developed through several phases: •Identification and selection of 50 key words •Creation of a dedicated section on APMARR's institutional website •Recording of explanatory videos, published on APMARR's YouTube channel, featuring rheumatology specialists and nurses explaining medical-scientific terms, as well as patients and caregivers explaining terms related to the emotional, social, and affective spheres. •Organization of a concluding in-depth webinar •Production of an online booklet as a vocabulary, listing approximately 90 words important in rheumatology for patients. Results: The project, which involved the participation of both medical personnel and patients and their caregivers, examined about 50 selected words, thanks to the preliminary survey, that were important for patients and caregivers related to their diagnosis and treatment in rheumatology. The explanatory videos by experts, doctors, nurses, psychologists, patients, and caregivers, who explained each word in the new rheumatology vocabulary, generated around 57,500 views and over 900,000 impressions. The concluding webinar broadcast on APMARR's channels generated around 87 views on YouTube and 423 on Facebook. Figure 1 Figure 2 Conclusion: APMARR has developed a new Vocabulary of Rheumatology to educate and inform patients and caregivers. The aim is to provide an interactive, user-friendly tool to convey correct, authoritative, and expertly edited information, helping them become familiar with the rheumatology landscape and making the concept of empowerment tangible. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.148 | 0.094 |
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".