Can Emergency Department Triage Nurses Appropriately Utilize the Ottawa Knee Rules to Order Radiographs?—An Implementation Trial
Bibliographic record
Abstract
OBJECTIVE: To determine whether triage nurses can successfully interpret the Ottawa Knee Rule (OKR) and order knee radiographs according to the OKR. METHODS: This was a prospective implementation trial of a clinical decision rule, set in a suburban, community emergency department (ED), evaluating a convenience sample of ED patients aged > 17 years with acute knee injuries. Patients were excluded for altered mental status, distracting injuries, and knee lacerations. Triage nurses and attending emergency physicians (EPs) were trained in appropriate use of the OKR. The triage nurses evaluated eligible patients and radiographs were ordered according to their interpretation of the OKR. EPs who were initially blinded to the triage assessments also evaluated the patients. EPs could add an x-ray order if, according to their assessment of the OKR, one was indicated and a radiograph had not been ordered by the nurse. Nurses and EPs recorded their blinded assessments on standardized data collection instruments. Kappa values were calculated to assess interobserver agreement (IOA) between nurses and EPs; sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) were calculated as appropriate. RESULTS: One hundred three patients were enrolled; 53% were female; 10 fractures were identified (9.7%). The IOAs between the nurses and EPs for each of the criteria were moderate to almost perfect: age-0.94; fibular head tenderness-0.4; isolated patellar tenderness-0.68; inability to bend knee to 90 degrees-0.73; inability to bear weight-0.76. The IOA was moderate (0.52) for the overall interpretation of the OKR by nurses and EPs. Sensitivity of nurse interpretation of the OKR for fracture was 70%, specificity 33%, NPV 91%, PPV 10%. Sensitivity of EP interpretation of the OKR for fracture was 100%, specificity 25%, NPV 100%, PPV 13%. CONCLUSIONS: Triage nurses showed fair to good ability to appropriately apply the OKR to pre-order knee radiographs.
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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 | 0.056 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".