Impact of web‐based clinical practice guidelines on paediatric fracture clinics
Bibliographic record
Abstract
BACKGROUND: In an effort to standardize management and reduce over-treatment of uncomplicated paediatric fractures, the Victorian Pediatric Orthopaedic Network and the Royal Children's Hospital, Melbourne, created publically available web-based paediatric fracture pathways. The aim of this study was to determine the impact of web-based fracture pathways on the clinic volume at a tertiary-care paediatric fracture clinic. METHODS: A comparative retrospective review was performed at a large, urban, tertiary-care children's hospital. Fracture clinic data from two 12-week periods before and after implementation of the fracture pathways were compared. For each study period, data collected included: total number of emergency department visits, number of fracture clinic visits, number of fracture clinic visits for patients that presented with upper extremity fractures for which web-based fracture pathways were available, number of radiology department visits for X-rays, and number of fracture clinic visits for patients requiring orthopaedic intervention in the operating room (closed or open reductions). RESULTS: The number of fracture clinic visits for patients with upper extremity fractures decreased 12% post-pathway implementation, from 954 visits to 842 visits. The number of radiology department visits for patients with upper extremity fractures decreased 24% post-pathway implementation, from 714 to 544 visits. CONCLUSION: The implementation of web-based fracture pathways for upper extremity paediatric fractures was associated with a decrease in clinic resource utilization at a tertiary-care children's hospital.
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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 | 0.011 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".