X-ray requesting patterns before and after introduction of the Ottawa Knee Rules in a UK emergency department
Bibliographic record
Abstract
OBJECTIVES: To compare knee radiology requesting rates among junior doctors before and after the formal introduction of the Ottawa Knee Rules (OKR) in a UK emergency department (ED), and to test the validity of the OKR for decisions on the use of radiography for acute, isolated knee injuries. METHODS: All junior doctors in a district general hospital ED seeing adult patients with isolated knee injuries completed a questionnaire before and after the introduction of the OKR. All patients were followed up to obtain a final diagnosis. The outcome measures were: adherence to the OKR, the presence of a fracture and whether a radiograph had been requested. The results were analysed to determine the sensitivity, specificity, positive and negative predictive values of the OKR. Comparisons between the request rate for knee radiography before and after the introduction of the OKR were made. RESULTS: A total of 130 patients were enrolled and followed up over a 3-month period; 58 before and 72 after OKR introduction. The OKR had a sensitivity of 100% (71.8-100%), a specificity of 55.1% (46.1-64.1%), a positive predictive value of 18.5% (9.03-27.9%) and a negative predictive value of 100% (87.8-100%) for the detection of bony injury. The introduction of the OKR did not result in a significant reduction in the number of radiographs performed (58.6-55.6%; P= 0.726). DISCUSSION: This study shows the OKR to be a highly sensitive clinical guide with a high negative predictive value in the setting of a UK ED. It suggests that the reduction in radiograph requests seen elsewhere may not be as apparent in this setting.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".