Musculoskeletal diseases, infections and vaccines: state of the art, research perspectives and educational needs
Bibliographic record
Abstract
Musculoskeletal disorders are a significant public health burden concern, projected to increase in the coming decades, and will substantially contribute to the rising prevalence of functional impairment, frailty and disability in a growing global population. Since persons with musculoskeletal disorders tend to have immune dysfunction, inflammation or be taking immunosuppressive medication, prevention of vaccine-preventable diseases (VPDs) in this group is particularly important. The European Interdisciplinary Council for Aging (EICA) and the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) jointly convened a 2-day in-person and virtual meeting on 26-27 September 2023, to review the state of the evidence on the link between musculoskeletal diseases, infections and vaccines. We present here the Executive Summary of the proceedings of this meeting. We review the importance of physical activity in preventing or mitigating both musculoskeletal diseases and risk of infection. We summarize current knowledge of the impact of common VPDs on the development and progression of musculoskeletal diseases, and the role of selected vaccines in preventing onset and worsening of frailty and disability in these individuals. This report summarizes the evidence presented at the two-day meeting, highlighting the need to raise awareness among scientists, healthcare professionals, decision-makers, civil society and the general public about the long-term sequelae of VPDs, with focus on the health status of older patients with musculoskeletal diseases.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".