Diagnostic algorithms for acute ankle injury imaging
Bibliographic record
Abstract
Ankle trauma is the most prevalent low extremity injury among urgent referral patients. Up to 85% of acute ankle traumas lead to an isolated ligament injury, with up to 50% of these patients would have chronic pain syndrome in the future, related to inaccurate diagnosis and resulting inappropriate treatment strategy and rehabilitation term. We analyzed publications on the state-of-the-art aspects of radiation diagnostics of acute ankle injury available from PubMed/MEDLINE databases and in the Russian Index of Scientific Citation (Elibrary.ru) for the last ten years; some earlier essential publications on certain aspects were also considered. Up to now, there have been no unified guidelines on the radiation diagnosis of ankle injury depending on the trauma type, mechanism, and severity. The Ottawa ankle rules (1994) are the basic guidelines for selection of the patients with acute trauma who should be offered X-rays. Primary X-ray would allow for the choice of the treatment strategy or further diagnostic assessment of the patient. Computed tomography is done for multi-fragment intra-articular fractures and for the control after their reposition. Computed tomography is used in patients with severe pain syndrome and other absolute and relative contraindications for magnetic resonance imaging. The latter allows for the imaging of all injured structures within a single assessment procedure and by such to make the diagnosis of ligament and tendon ruptures, to visualize osteochondral injuries, hidden and stress fractures and many other acute ankle injuries. Ultrasound assessment can considerably add to clinical understanding of the patient during acute trauma, if magnetic resonance imaging is contraindicated. Based on the analysis performed, we propose the algorithms for diagnostic assessment in various clinical situations.
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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.018 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".