Clinical and Radiographic Dependences of Functional Status, Indices of the Hip Joint, and Femur Migration in Children with Cerebral Palsy
Bibliographic record
Abstract
Relevance: Significant incidence of hip pathology in different groups of children with cerebral palsy and factors that may affect its formation are relevant objects of the study. The Goal of the Study: To establish the features of the hip joint’s formation, examining the clinical and radiographic dependences of the functional status and indices of the hip joint in patients with cerebral palsy. Materials and Methods: We conducted a clinical and radiographic examination of the hip joints using our own methods and standard anterior-posterior radiography, and statistical analysis of hip parameters and factors that may have influenced their formation. The total number of patients was 47 persons (86 joints). Results: Correlation relationships have been established between hip parameters and factors that may affect them: Gross Motor Function Classification System (GMFCS), gait function, level of lesion, developmental dysplasia of the hip, and adductor myotomy in medical history. Conclusions: The Reimers’ index showed greater reliability compared to the Wiberg angle. Positioning the patient's body using our own method can be used to screen the hip joints in cerebral palsy based upon the Reimers index while obtaining the true parameters of the femoral neck-shaft angle and torsion of the femur.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".