Comparison of Shetty ankle test and Ottawa ankle rules in ankle injuries.
Bibliographic record
Abstract
OBJECTIVE: Foot and ankle injuries are the most common extremity trauma and have an important place among the admissions to the emergency service. Ankle injuries are the most frequent form of orthopedic emergencies. Currently, Ottawa ankle rules (ORL) is the most popular test used worldwide for the ankle injuries. Shetty test is an important test used to evaluate the clinical status of patients admitted for the ankle trauma. PATIENTS AND METHODS: This study was initiated in the emergency service between May 1, 2018, and May 1, 2019, after the approval of the Ethics Committee. The patients were classified according to gender, age, and admission, Ottawa test results, Shetty test results, and radiography status. RESULTS: Shetty test can be used to differentiate between the foot sprains and fractures as a simple and inexpensive method. Despite its effectiveness, Ottawa ankle rules is not widely used due to the detailed implementation of the Ottawa ankle rules and the components of the test. The ease of applicability of the Shetty test in primary care makes the test stand out. In our study, although the sensitivity of Shetty and Ottawa tests resulted in close percentages (82.86% - 85.71%), the specificity of Ottawa test was found to be higher. CONCLUSIONS: Detecting a fracture in emergency services is more valuable than excluding a fracture. Even though Shetty test is simple to use, we recommend Ottawa test for the foot and ankle sprains due to the legal problem that could be caused by Shetty test.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".