Review article: Best practice management of common ankle and foot injuries in the emergency department (part 2 of the musculoskeletal injuries rapid review series)
Bibliographic record
Abstract
Ankle and foot injuries are the most common musculoskeletal injuries presenting to Australian EDs and are associated with a large societal and economic impact. The quality of ED care provided to patients with ankle and foot fractures or soft tissue injuries is critical to ensure the best possible outcomes for the patient. This rapid review investigated best practice for the assessment and management of common ankle and foot injuries in the ED. Databases including PubMed, CINAHL, EMBASE, TRIP and the grey literature, including relevant organisational websites, were searched in 2017. Primary studies, systematic reviews and guidelines were considered for inclusion. English language articles published in the last 12 years that addressed the acute assessment, management or prognosis in the ED were included. Data extraction of included articles was conducted, followed by quality appraisal to rate the level of evidence where possible. The search revealed 1242 articles, of which 71 were included in the review (n = 22 primary articles, n = 35 systematic reviews and n = 14 guidelines). This rapid review provides clinicians managing fractures and soft tissue injuries of the ankle and foot in the ED a summary of the best available evidence to enhance the quality of care for optimal patient outcomes. Following a thorough history and physical examination, including the application of the Ottawa ankle rules, ED clinicians should not only provide a diagnosis, but rate the severity of soft tissue injuries, or stability of fractures and dislocations, which are the pivotal decision points in guiding ED treatment, specialist referral and the follow-up plan.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".