Self‐Reported Issues With Driving in Patients With Chronic Pain
Bibliographic record
Abstract
OBJECTIVE: To assess the driving habits, driving patterns, and barriers to driving reported by patients with chronic pain. DESIGN: Cross-sectional mail survey with self-administered questionnaires. SETTING: University-affiliated hospital. PARTICIPANTS: A sample of 223 patients seen in consultation by a physiatrist through the Chronic Pain Rehabilitation Service. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASUREMENTS: Percentage of subjects who were current drivers, percentage of subjects experiencing difficulty with driving, and driving characteristics. RESULTS: Response rate was 48.9%. Of the subjects, 79% were current drivers; of the nondrivers, 56% reported stopping driving because of chronic pain. A significantly greater percentage of nondrivers (80%) than drivers (62.9%) were women (P = .039). Nondrivers reported greater levels of pain than drivers (P = .027). The mean Pain Disability Index total score was significantly lower for drivers (42.3) than for nondrivers (48.7; P = .006). Of all subjects, 70% indicated that pain limited their driving in some manner; 41% of this group indicated that they experienced quite a bit or a great deal of difficulty driving. Factors that limited driving included pain (88.9%), fatigue (50.6%), limited joint mobility/stiffness (48.3%), and weakness (19.4%). The most frequently reported difficulties related to driving were sitting for any length of time (79.6%) and getting into the driver's seat (66.5%). Only 2.4% of current drivers had been referred for a driving assessment. CONCLUSIONS: Most people with chronic pain continue to drive and overall appear to have better functioning than those who cannot continue driving because of chronic pain. Despite being able to drive, a significant proportion of drivers with chronic pain are facing challenges not only with driving the vehicle but also with entering and positioning themselves within the vehicle. Our results suggest that chronic pain does have an impact on driving. However, it appears to be generally unrecognized as a factor for driving other than when the implications of opioid use are considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".