Review of Diagnostic Accuracy of the Ottawa Knee Rules in Adult Acute Knee Injuries
Bibliographic record
Abstract
Acute knee injuries are commonly found in athletic populations.The wide range of knee injuries can present a challenge deciding which injuries require imaging.Currently, radiography is considered the gold standard of imaging for knee fractures.However, imaging is often costly not only for the patient, but also for the healthcare system.Since imaging is expensive, it is critical that clinicians decipher which patients require such testing.The Ottawa Knee Rules (OKR) is a predictor tool created by Stiell et al in 1995 to help clinicians accurately distinguish potential knee fractures from non-fractures for imaging purposes.The OKR guidelines state that a patient should be referred for radiography if he/she meets at least one of the following criteria: (1) 55+ years of age (2) tenderness over fibular head (3) secluded pain on the patella (4) cannot flex knee to 90 degrees (5) cannot bear weight for at least four steps.The OKR has been used in clinical settings to rule out knee fractures in patients.The ability of OKR to accurately differentiate knee fractures from non-fractures has been investigated to determine how effective the tool is.This article examined eight research studies including over 7,000 participants to determine the diagnostic accuracy of OKR in adults.This examination showed that OKR exhibited high accuracy in diagnosing knee fractures needing imaging.The OKR demonstrated a sensitivity of 0.99, specificity of 0.49, LR+ of 1.86, and LR-of 0.07.This data indicates a confidence interval (CI) of 95%.Furthermore, OKR showed low risk of bias and was beneficial to reducing medical costs and medical wait times.More than half (5) of the studies indicated a reduction rate in imaging completed when utilizing OKR.Therefore, OKR is a beneficial and accurate tool to implement in clinical decision-making when making imaging referrals for acute knee injuries in adults.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".