Detection Rate of Fractures by Triage Nurses Applying the Ottawa Foot Rule
Bibliographic record
Abstract
Aim: The purpose of this study was to investigate the detection rate of fractures by triage nurses by applying the Ottawa foot rule.Materials and Methods: This is a prospective observational validation study that was designed in a training and research hospital between January and December 2013 on 98 patients with isolated foot injury. After triage nurses were provided training on the Ottawa foot rule for 4 hours, they evaluated patients with foot trauma by applying the rule. Foot radiographs were obtained from all trauma patients who were evaluated in the triage. Radiographs were evaluated by an emergency medicine specialist and fractures were determined.Results: Data collection procedures included the evaluation of 90 out of 98 patients who demonstrated one or more qualities of the Ottawa foot rule according to the trained nurses. The fracture prediction rate of the triage nurses using the Ottawa foot rule was found to be 14.4%. The sensitivity of the “the inability to take four steps in the Emergency Department” was 100% and the specificity was 41.6%. Of all patients, 69.2% with fractures were aged <55 years, while 30.8% were aged ≥55 years.Conclusion: This study revealed that triage nurses could successfully perform the Ottawa foot rule after a brief training. According to the Ottawa foot rule applied by the triage nurses, the “inability to take four steps in the Emergency Department” rule was found to be the most significant.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".