Are cardiac patients in Saudi Arabia provided adequate instructions when they should not drive?
Bibliographic record
Abstract
Abstract Objective: Driving capability can be significantly affected by different heath disorders; cardiovascular diseases (CVDs) should be considered when assessing patients for medical fitness to drive (MFTD). The aim of this study was to evaluate the awareness of Saudi patients about driving recommendations and to assess the incidence of motor vehicle accidents (MVAs) among cardiac patients. Materials and Methods: We conducted a cross-sectional survey-based study. Male patients diagnosed with CVDs and who were visiting outpatient departments were invited to complete a questionnaire regarding their awareness of driving recommendations. Patients’ demographics, clinical diagnosis, echocardiography parameters, and time-to-CVD diagnosis were all obtained from the patients’ medical records. Women were excluded because it was illegal for women to drive in Saudi Arabia during the study period. Results: In total, 800 men were included, with a mean age of 54 ± 12 years. Driving counseling had been provided to 241 participants (30%). Of these, 207 (25%) were advised not to drive for a period of between one week and six months. Five percent of the patients had a history of MVAs during the follow-up period of 6.2 ± 4 years. We found that the presence of a dyspnea ≥2, according to the New York Heart Association (NYHA), and a history of loss of consciousness (syncope/pre-syncope) were significantly associated with accidents (46% vs. 20%, P < 0.0001 and 41% vs. 10%, P < 0.0001, respectively). Conclusion: Patient–physician discussion about MFTD was only performed with 30% of the patients with CVDs in Saudi Arabia. Dyspnea NYHA class ≥2 or a prior history of syncope were significantly associated with the incidence of MVAs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".