Driving After Traumatic Brain Injury: Closing the Gap Between Assessing, Rehabilitating and Safe Driving
Bibliographic record
Abstract
The privilege of driving a vehicle is often a fundamental part of individuals’ daily lives. For many individuals who have suffered a traumatic brain injury (TBI), the ability to return to driving post TBI is an integral step to recovering independence and enhancing community reintegration (Rapport et al., 2008). Approximately 50% of TBI survivors with moderate to severe injuries resume driving, often irrespective of medical-legal evaluations (Fisk, Schneider, & Novack, 1998; Lew et al., 2005; Tamietto et al., 2006). Evidently, helping TBI survivors return to safe driving plays a pivotal role in their path to recovery and reintegration to the community. A proper assessment of a TBI survivor’s strengths and weaknesses can help prevent harm to the driver and other members of society and further enable their return to productive roles, work, and other favored activities. For instance, Kreutzer and colleagues (2003) revealed that the ability to drive post TBI is an independent moderator for employment stability. Determining whether a TBI survivor is safe or unsafe to drive remains a challenging issue since driving is a functional task with varying levels of complexity that can be potentially compensated for if impairments exist. Unfortunately, two negative outcomes may occur as a result of inaccurate driving assessment. The first negative outcome may be removing the privilege to drive from a TBI survivor who is either safe to drive, or could become safe to drive after retraining or further recovery (false positive result). The second outcome is a false negative result where the brain injury survivor is a potentially unsafe driver who is allowed to resume driving. Previous research suggests that TBI drivers tend to receive greater traffic violations (Haselkorn et al., 1998), tend to drive slower (in a simulated environment; Stinchcombe et al., 2008), and perhaps most importantly, have an increased crash risk compared to uninjured controls (e.g., Formisano et al., 2005; Lundqvist et al., 2008; but see Haselkorn et al., 1998; Schultheis et al., 2002). For example, Schanke and colleagues (2008) assessed driving behaviour of TBI survivors both pre and post injury. Results indicated that the accident rate of the TBI survivors was twice as high as that of the general population. Cyr and colleagues (2009) observed that in a simulated driving environment, TBI survivors who had returned to driving, compared to uninjured controls, were significantly more likely to crash in reaction to a surprising and challenging event.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".