Evaluation the accuracy and cost effectiveness of using the Bernese and Ottawa rules in ankle Sprain.
Bibliographic record
Abstract
INTRODUCTION: Ankle sprain is one of the most common musculoskeletal injuries that occur most commonly in the active and working population. Ottawa and Burns Laws are tools for physicians to determine the need for a radiograph of an ankle injury. The purpose of this study was to evaluate the accuracy of the Ottawa and Bernese criteria in patients with torsion of the foot and the economical savings resulting from the application of these two criteria. METHODS: This prospective study was designed to evaluate the two rules burns of Bernese and Ottawa and their economic savings were designed in two phases. They were referred to Poursina Medical Center, Rasht, Iran from September 2019 to the achieved sample size. Data were analyzed by SPSS software version 24 (Statistical Package for Social Science (SPSS) 21, Chicago, IL, USA). RESULTS: A total of 800 patients were included in this study to determine the accuracy of bronze and Ottawa criteria in ankle torsion and the economic cost of using them. Of the 800 patients studied, 430 (53.7%) were male and 370 (46.3%) were female, with a mean age of 35.77±16.42 years. The diagnostic accuracy of the Ottawa criteria is 90% and the diagnostic accuracy of the Bernese criteria is 90.75%. The sensitivity of the Ottawa evaluation method was 97.6% and the specificity was 88%. The sensitivity of Bernese evaluation method was 91% and specificity was 90.7%. CONCLUSION: Because of the higher sensitivity of the Ottawa criterion than the Bernese criteria, it is preferred to determine the probability of fracture for emergency unit personnel. Using these two methods can reduce the time, energy and cost of treatment for the patient during the treatment period.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.000 |
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".