Evidence for targeting autonomic dysfunction in systemic sclerosis: A scoping review
Bibliographic record
Abstract
Autonomic dysfunction is a common and early complication among patients with systemic sclerosis, suggesting that it may play a role in the pathogenesis of the disease and be a potential target for therapeutic interventions. Although the true prevalence of autonomic dysfunction among patients with systemic sclerosis is still unclear, it is estimated that as many as 80% of patients may be affected. Autonomic dysfunction may lead to widespread multi-organ dysfunction through its effects on the cardiovascular system, gastrointestinal tract, urinary tract, sweat and salivary glands, and pupils. Early identification of systemic sclerosis associated with dysautonomia may guide prompt diagnosis in this complex patient population and lay the groundwork for future research in this area. Furthermore, the current landscape of targeted interventions for autonomic dysfunction is rapidly expanding; therefore, prioritizing patients who may benefit from such interventions or candidates for related clinical trials is paramount. Our scoping review details timely updates in the extant literature, including findings from recent studies on autonomic dysfunction in systemic sclerosis, and integrates these updates to identify critical gaps in the field.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".