Use of Sonography in the Diagnosis of Developmental Dysplasia of the Fetal Hip
Bibliographic record
Abstract
Historically, plain radiographic film examinations were the norm for diagnosing developmental dysplasia of the hip (DDH); but today, sonography has also proven to be a safe and effective alternative (before the bones have ossified) to using plain film. This article outlines important features of DDH, such as risk factors and clinical signs, along with a look at the Ortolani and Barlow stress manoeuvres done by physicians for DDH screening. The unique ability of sonography to mimic these manoeuvres during examinations is outlined, along with the steps to completing an ultrasound examination for DDH. The differences between the European and Canadian protocols are also noted. Furthermore, the important anatomy of the fetal hip is explained, along with its normal and abnormal appearances. During diagnosis, the severity of the condition is now determined using the Graf classification system, which is outlined. Because sonography has become the primary diagnostic tool for DDH, it is important to be familiar with the proper methods needed for screening. Due to the vast abundance of research done on DDH, this article focuses on key information most relevant to sonographers.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".