Assessment of the frequency and severity of foot deformities in patients with rheumatic diseases
Bibliographic record
Abstract
Background: Rheumatic diseases manifest with inflammatory and degenerative changes in the musculoskeletal system, leading to joint deformities, limiting dexterity, causing pain, and being a significant factor affecting disability level.Common causes of foot deformities include arthropathic conditions such as rheumatoid arthritis (RA), ankylosing spondylitis (AS), and some connective tissue diseases such as systemic sclerosis (SS).Aims: The study aimed to assess the frequency of flat feet occurrence and the regularity of foot structure among patients with rheumatic diseases. Material and methods:The study included patients suffering from three rheumatic diseases: RA, AS, and SS.The conducted research involved a static assessment of the plantar side of the feet, consisting of creating a detailed imprint using plantar contouring and measurements of podometric parameters for both feet, utilizing a computerized recorder integrated with a podoscope. Results:In over half of all patients, the presence of valgus deformity of the foot was observed (right foot -54.4%, left foot -52.6%), while a small percentage exhibited varus deformity of the foot (both right and left feet -2.3%).Normal foot arching was found in just under half of the participants (right foot -43.3%, left foot -45.1%).Only 24 (14.0%)individuals confirmed awareness of flat feet.Conclusions: There is a need to assess patients with rheumatic diseases for the presence of functional disorders of the foot to implement early exercise prevention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".