Shetty Test Challenges Ottawa Ankle Rules in Detecting Foot and Ankle Fractures: A Prospective Comparative Study.
Bibliographic record
Abstract
Objectives: Ankle joint injuries are among the most common orthopedic injuries and are associated with significant healthcare costs. To reduce unnecessary radiographic screening, diagnostic tools such as the widely accepted Ottawa Ankle Rules (OARs) have been developed. However, the accuracy of OARs in excluding fractures remains uncertain. Recently, a new diagnostic test, the Shetty Test (ST), has been introduced. This prospective comparative study aimed to evaluate the diagnostic accuracy of the "ST" in comparison to the "OARs" for detecting ankle and foot fractures. Methods: A total of 112 consecutive adult patients (>18 years old) were included in the study. They were presented to the Emergency Department of a University Hospital in Alexandroupolis due to an ankle or foot injury. Data were collected over 6 months, from November 2022 to May 2023. Results: The sensitivity of the ST was 68.4%, specificity was 76.3%, positive predictive value (PPV) was 37.1%, and negative predictive value (NPV) was 92.2%. For the OARs, sensitivity was 94.7%, specificity was 15%, PPV was 18.5%, and NPV was 93.3%. When at least one of the tests was positive, the sensitivity and NPV increased to 100%. Conclusion: The ST was found to be reliable; however, it did not outperform the OARs in this study. Nevertheless, when used in conjunction, the two tests significantly improved sensitivity and the NPV. Due to its simplicity and reproducibility, the ST could be a valuable tool in daily clinical practice, particularly for non-orthopedic emergency department personnel.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".