A Novel Smartphone App to Support Learning and Maintaining Competency With Bier Blocks for Pediatric Forearm Fracture Reductions: Protocol for a Mixed-Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Distal forearm fractures are among the most common injuries presenting to the pediatric emergency department (PED). Bier block (BB), or intravenous regional anesthesia, is a safe and effective alternative to procedural sedation for closed reduction of forearm fractures; it is associated with fewer adverse events, a shorter length of stay, and reduced costs. BB has long remained relatively underutilized; however, with an increasing emphasis on efficient resource use and patient-centered care, there is renewed interest in this technique. OBJECTIVE Our tertiary PED recently became the first in Canada to successfully implement an active BB program. Subsequently, we developed a mobile BB smartphone app designed to support the sustained departmental use of BB. The app can be used for training and maintenance of competency and incorporates instructional material, as well as our institutional BB protocol, printable medication order sheets, and monitoring forms. The present report describes the development and functionality of the BB smartphone app. METHODS We have described app development and content. App dissemination metrics will be tracked, and user feedback will be analyzed using a self-administered electronic survey. Additionally, app utilization in our PED will be compared with real-world clinical use of BB for fracture reductions. RESULTS The first iteration of the BB app was launched in 2015, with the most recent update in September 2018. App metric tracking is planned for January 2020 until December 2021. CONCLUSIONS We have highlighted how the BB app serves as a paradigm of an educational tool designed not only for individual users but also for supporting the department-wide implementation and dissemination of a new technique. App dissemination and use metrics will be tracked and correlated with clinical use of BB in the PED. INTERNATIONAL REGISTERED REPOR PRR1-10.2196/10363
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.069 | 0.012 |
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".