Canadian Society of Plastic Surgeons / Société Canadienne Des Chirurgiens Plasticiens: 76 <sup>th</sup> Annual Meeting / 76 <sup>e</sup> Réunion annuelle June 13-17 juin 2023 Whistler, BC / Whistler, C-B
Bibliographic record
Abstract
Purpose: The Calgary Kids' Hand Rule (CKHR) is a clinical prediction rule intended to guide referral decisions for pediatric hand fractures presenting to the emergency department, identifying 'complex' fractures that require surgical referral and optimizing care through better matching of patients' needs to provider expertise.The objective of this study was to externally validate the CKHR in two different tertiary pediatric hospitals in Canada.Method: We partnered with British Columbia Children's Hospital (BCCH) and the Children's Hospital of Eastern Ontario (CHEO) to externally validate the CKHR using data from retrospective cohorts of pediatric hand fractures (via electronic medical record and x-ray review).Model performance was evaluated at each site using sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and the C-statistic.Results: A total of 954 hand fractures were included in the analysis (524 at BCCH and 430 at CHEO.At BCCH, the CKHR had a sensitivity of 91.1% (133 predicted complex out of 146 total complex fractures), specificity of 71.4% (269 predicted simple out of 377 total simple fractures), and C-statistic of 0.81.At CHEO, the CKHR had a sensitivity of 98.3%, specificity of 30.2%, and C-statistic of 0.64. Conclusion:The CKHR performed well at two different tertiary care centres with high sensitivity, supporting its ability to facilitate hand fracture triage in other populations without further modification.This work should be followed by rigorous implementation analysis to determine its impact on patient care.Learning Objectives: At the end of this presentation, the learner will be able to: 1. Identify the Calgary Kids' Hand Rule as a prediction rule for pediatric hand fractures.2. Describe the process of external validation of a clinical prediction rule.3. Explain what it means that the prediction rule had a high sensitivity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.171 | 0.036 |
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".