THE OTTAWA AND PITTSBURGH RULES FOR SELECTIVE RADIOGRAPHY FOLLOWING ACUTE KNEE INJURY
Bibliographic record
Abstract
Radiographs are frequently ordered following acute knee injury. However, it is suggested that only 6 % of patients with a knee trauma have a fracture. Decision rules such as the Ottawa rules and the Pittsburgh rules have been developed to reduce the unnecessary use of radiographs following knee injury. We prospectively reviewed all acute knee injury patients who were referred to our clinic from the emergency department over a 3 month period. The reason for ordering radiographs was analysed. The Ottawa and the Pittsburgh rules were applied to individual patients to evaluate the need for radiographs. In patients with a diagnosis of fracture, the accuracy of the Ottawa and the Pittsburgh rules was studied. A total, of 106 patients were referred to the acute knee clinic from the emergency department. 95.28 % (101) of these patients had radiographs of their knee in the emergency department. Five (4.72%) patients had a fracture of their knee and all these cases, the Ottawa and the Pittsburgh knee rules for ordering radiographs was fulfilled. In a vast majority of cases without any fracture, the clinical reason for ordering radiographs was not clear. Using the Ottawa rules for knee radiography 25.47% (27) radiographs could be avoided without missing a fracture. Using the Pittsburgh rules, 30.19 % (32) knee radiographs could be avoided without missing a fracture. The Ottawa and the Pittsburgh rules have a high sensitivity for the detection of knee fractures. Use of these rules can aid efficient clinical evaluation of the knee in an emergency situation without adverse clinical outcome. They may also have an implication on reducing the work load of radiology department and reduction of health costs.
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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 | 0.021 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".