“When can I return to driving?”
Bibliographic record
Abstract
Clinicians are often asked by patients, "When can I drive again?" after lower limb injury or surgery. This question is difficult to answer in the absence of any guidelines. This review aims to collate the currently available evidence and discuss the factors that influence the decision to allow a patient to return to driving. Medline, Web of Science, Scopus, and EMBASE were searched using the following terms: 'brake reaction time', 'brake response time', 'braking force', 'brake pedal force', 'resume driving', 'rate of application of force', 'driving after injury', 'joint replacement and driving', and 'fracture and driving'. Of the relevant literature identified, most studies used the brake reaction time and total brake time as the outcome measures. Varying recovery periods were proposed based on the type and severity of injury or surgery. Surveys of the Driver and Vehicle Licensing Agency, the Police, insurance companies in the United Kingdom and Orthopaedic Surgeons offered a variety of opinions. There is currently insufficient evidence for any authoritative body to determine fitness to drive. The lack of guidance could result in patients being withheld from driving for longer than is necessary, or returning to driving while still unsafe.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".