Utility of Ottawa ankle rules in excluding ankle fractures in Indian scenario
Bibliographic record
Abstract
Background: Patients with acute ankle injuries form a major bulk in outdoor and emergency room, and many of them get radiographs done to rule out fractures. Ottawa ankle rules (OAR) may reduce the need for unnecessary radiographs by detecting fractures only with help of simple clinical findings. We conducted this study to see the extent of usefulness of these rules in our day-to-day practice. Methods: Our study is observational in nature. A total of 107 patients who visited the clinic of the chief investigator between the time period from 1st January 2019 to 31st December 2020, fulfilling inclusion criteria and willing to participate, were enrolled. The patients were examined clinically, and the assessor recorded the findings on a previously prepared assessment form. Data analysis was done from the master chart. Results: Among the 107 patients, 46 patients were ‘suspicion positive’ by OAR. After the radiographic assessment, we found 11 fractures, all of which belonged to the ‘suspicion positive’ group. Statistical analysis showed that OAR had a sensitivity of 100% for ankle fractures, whereas specificity for the same was 63.54%. We found the positive predictive value to be 23.91% and negative predictive value to be 100%, positive likelihood ratio of 2.74, and negative likelihood ratio of 0. Conclusions: OAR is an easy and reliable tool to screen ankle fractures. In a country with as massive a health care burden as ours, it can reduce the number of unnecessary radiographs and thus reduce exposure, cost, and time of medical professionals.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".