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An Examination of Screening Practices for Developmental Dysplasia of the Hip Across Sri Lankan Medical Specialties

2025· article· en· W4407200152 on OpenAlexaff
D. De Silva, Dimuthu Chrishantha Tennekoon, Shaman Rajindrajith, A.C.D. Rajapaksha, Alaric Aroojis, Kishore Mulpuri, Emily K. Schaeffer, Sunil Ranjith Wijayasinghe

Bibliographic record

VenueJAAOS Global Research and Reviews · 2025
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsSri lankaSpecialtyMedicineContext (archaeology)Health careFamily medicineNursingPolitical scienceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.149
GPT teacher head0.511
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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