Bibliographic record
Abstract
SUMMARY Orthopedic procedures or injuries can temporarily prevent patients from driving. The time duration until they can resume driving has significant financial, medico-legal and legal implications on the patient, physician and society. There are a few guidelines about driving restrictions following acute lower limb events, however, the time duration varies among jurisdictions. The current published recommendations vary from no driving abstinence to 9 weeks, depending on the affected side, procedure, immobilization, pain level and type of car (automatic vs manual). There is also individual variability in patients with respect to their pain, comorbidities and prior driving experience. The decision to allow a patient to drive is often made clinically by the treating physician, but there is no consensus among orthopedic surgeons about driving restrictions. The use of opioid analgesic medications is regarded as an important factor in the decision to restrict driving. In this article, we review the current guidelines and clinical studies available for acute lower limb injuries or interventions including total hip arthroplasties and total knee arthroplasties, knee arthroscopies, anterior cruciate ligament reconstruction and lower extremity fractures.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".