Methodological Challenges in Investigating Supracondylar Fractures of the Humerus From a Child’s Viewpoint: Evolution of Study Protocol
Bibliographic record
Abstract
BACKGROUND: Outdoor play and risk-taking behaviors, including play at heights, are important to children's physical, social, and cognitive development. These aspects of play are important to consider when informing prevention policies for serious injuries that commonly occur on play structures. Supracondylar fractures of the humerus (SCH) are the most common type of elbow fractures that result from falls on an outstretched hand among healthy children. Despite being one of the leading causes of admission to the hospital and surgical intervention, the details surrounding the cause of these injuries are often not recorded. Previous research has correlated decreased overall playground safety with higher rates of SCH fractures. Play structure height and the type of undersurface have been identified as potential risk factors for severe injuries, including SCH fractures, in part due to low compliance with safety standards. This paper explores the challenges we encountered designing the study and the resulting insights and methodological modifications we made. OBJECTIVE: The aim of this paper is to discuss the challenges related specifically to clinical research in pediatrics and strategies developed to conduct a study that prioritizes the engagement and perspective of children and their families. METHODS: To explore the link between the severity of SCH fractures and children's behavioral, environmental, and mechanistic factors, we conducted a mixed-methods study. RESULTS: During phase 1 (the original methodology) from April 2017 to July 2018, there were 58 eligible study participants and 17 were recruited. For phase 2 (the revised methodology) between October 2018 and October 2019, there were 116 eligible participants and 47 were recruited. CONCLUSIONS: The changes in methodology made following the first phase of data collection were effective in our ability to recruit participants. By identifying and addressing challenges pertaining to recruitment and resource limitations, we were able to collect data in a concise manner while not compromising the quality of the data and make for an easily adoptable methodology for other sites interested in participating in the study. We hope that future studies that plan to employ a similar methodology can gain insight through the methodological challenges we have encountered and the way we adapted the methodology to build a more pragmatic approach. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21816.
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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 | 0.304 | 0.322 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".