Pediatric ankle injuries result in more than2 million emergency department visits inCanada and the United States each year
Bibliographic record
Abstract
Information: unpublished data, 2007).1,2 Radio-graphs are ordered for 85%–95 % of these chil-dren,3 although only 12 % of these reveal a frac-ture.4 Thus, radiography is unnecessary for most children’s ankle injuries, and these high rates of radiography needlessly expose children to radia-tion and are a questionable use of resources. The Low Risk Ankle Rule has 100 % sensitiv-ity with respect to identifying clinically important pediatric ankle fractures and has the potential to safely reduce imaging by about 60%.4 When the application of the rule suggests that radiography is not needed, it has been shown that any frac-tures that might be missed are clinically insignifi-cant and can be safely and cost- effectively man-aged like an ankle sprain, with superior functional recovery.5 Finally, the Low Risk Ankle Rule has been shown to have excellent accept-ability when tested on emergency physicians.6 The main objective of this study was to imple-ment the ankle rule in several different emer-gency department settings using a multimodal knowledge translation strategy and to evaluate its impact on the frequency of radiography in chil-dren presenting with acute ankle injuries. Methods Study design and settings We conducted this study over an 18-month period at 6 Canadian emergency departments, using an interrupted time series with pair-matched control design.7 Participating sites rep-resented a convenience sample of 6 hospitals located in Ontario, Canada. We selected inter-vention sites based on the availability of infra-
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".