Driving problems in patients with rheumatoid arthritis.
Bibliographic record
Abstract
OBJECTIVE: To assess driving problems experienced by patients with rheumatoid arthritis (RA) and to examine the relationship between functional status and driving difficulty. METHODS: Using the South Eastern Ontario Medical Organization (SEAMO) database, we identified 721 patients with RA from both urban and rural backgrounds. They completed a cross-sectional, self-administered mail survey that included the Health Assessment Questionnaire (HAQ-DI) and a co-morbidity questionnaire. We assessed the proportion of drivers versus non-drivers and patients who reported difficulty driving and who used vehicle adaptations. RESULTS: Survey response rate was 74% and 92.2% of the subjects were current drivers. Fifty percent of the current drivers reported a little difficulty, 6.8% reported quite a bit of difficulty, and 1.5% a great deal of difficulty driving. Major reasons given for why RA limited their driving were stiffness and pain. Frequent use of mobility aids (adjusted odds ratio, OR: 5.85), HAQ-DI > or = 1 (adjusted OR: 3.40), and older age (adjusted OR: 1.04) were significant predictors of an individual with RA discontinuing driving. Higher levels of disability (HAQ-DI) were associated with a greater number of problems reported with driving and with curtailment of driving. A multivariate logistic regression determined that having a HAQ-DI > or = 1 (adjusted OR: 4.3) and difficulties sitting in the vehicle (adjusted OR: 2.9) were associated with RA limiting driving. CONCLUSION: Over 50% of respondents reported some degree of difficulty driving due to their RA. Scores on HAQ-DI > or = 1 were associated with difficulty driving. Further validation of our findings needs to be performed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".