Rheumatoid arthritis in Lithuania: Need for external help from the onset of disease
Bibliographic record
Abstract
PURPOSE: To estimate the burden of rheumatoid arthritis (RA) in Vilnius, Lithuania, the former socialist country in Eastern Europe, in terms of patients' need for help from other persons and to explore the factors which influence the need for physical help. METHOD: Some 537 patients with RA, registered in Vilnius, answered questions about socio-demographics, disease characteristics, categories of required help, the use of major appliances and adaptations, underwent a clinical examination and filled in the modified health assessment questionnaire (MHAQ) and arthritis impact measurement scale (AIMS). Logistic regression was used to assess which variables from those explored influenced the need for physical help. RESULTS: A total of 230 (42.9%) patients out of 537 were requiring help from other persons, and the proportion was equally high in all the disease duration categories. A quarter of the patients (25.1%) were classified to ACR III and IV functional impairment groups. In multivariate logistic regression model the risk to become dependent on external help ultimately depended on MHAQ (10.32 [CI 95% 6.57; 16.23], p < 0.001) but the use of joint stabilization measures (1.97 [CI 95% 1.06; 3.64], p < 0.01) and 28 tender joints count (1.02 [CI 95% 1.0; 1.06], p < 0.05) were also important. CONCLUSIONS: Nearly half of the patients reported being dependent on others and a quarter of patients were in definite need for that. The functional impairment is the most important risk factor, although identifying the group using joint stabilization measures routinely may be of practical value in order to define the risk group which may need the external help in future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".