An Examination of Screening Practices for Developmental Dysplasia of the Hip Across Sri Lankan Medical Specialties
Bibliographic record
Abstract
INTRODUCTION: Sri Lanka has no formal care pathway to assess for developmental dysplasia of the hip (DDH), which may lead to later diagnosis, more invasive treatments, and long-term adverse health outcomes. With the goal of developing a care pathway for DDH in Sri Lanka, we first surveyed relevant medical specialties regarding their experience with DDH screening, diagnosis, and treatment to understand current screening and diagnosis practices. METHODOLOGY: A panel composed of four members affiliated with three Sri Lankan organizations collaborated with our team of researchers to inform the development of three specialty specific surveys. We distributed the surveys electronically to radiologists, pediatricians, neonatologists, and orthopedic surgeons. RESULTS: From the surveys, we gained an understanding of the present screening procedures. We identified potential areas to improve screening and diagnosis, including investigating the reliability of ultrasound (US) imaging reports and providing guidance on conducting appropriate referrals. DISCUSSION: These results will help inform the development of a DDH care pathway specific to the local context and needs in Sri Lanka. CONCLUSION: Care pathway development must be mindful of resource availability and strive to increase awareness of best care practices among healthcare practitioners.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".