Validation of the Ottawa Ankle Rules in Indian Scenario
Bibliographic record
Abstract
BACKGROUND: Ankle injuries are one of the most common presentations in emergency department. Ottawa Ankle Rules (OARs) have been used to predict the requirement of radiographs. OBJECTIVES: This study aimed to validate the OARs protocol for predicting ankle and midfoot fractures in Indian population. PATIENTS AND METHODS: A prospective study was conducted in a teaching hospital in north India, during a period of nine months, including all patients who presented with complaints in the ankle region and evolution of less than 48 hours. The study excluded patients with multiple trauma and Glasgow coma scale of less than 15. All patients underwent clinical evaluation, followed by radiographs depending upon the location of the complaints. Radiographic study results were evaluated by orthopedic surgeons who had not seen the patient. RESULTS: We evaluated 140 patients (84 males and 60 females) with the mean age of 35.2 (range, 8 - 76 years). Of the 140 evaluable patients, 71 had positive criteria for radiological evaluation of which 43 presented with fracture, 69 had negative criteria for radiography with no fracture. The sensitivity of OARs to detect fractures was 100%. The implementation of the OARs appears to have the potential to reduce the number of radiographs for the assessment of these patients by about 51%. CONCLUSIONS: The implementations of OARs have the potential to reduce the number of X-ray graphics needed to assess these patients by about 51%. The results of this study demonstrate no false negatives and are in agreement with results from other similar studies. It encourages us to implement these criteria in our services urgently, with all the resulting socio-economic implications.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".