Application of statistical shape modeling to the human hip joint: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This review aims to identify all examples of the application of statistical shape models to the human hip joint, with a focus on methodology, validation, and applications. INTRODUCTION: Abnormal hip joint morphology (eg, deformity secondary to Legg-Calvé-Perthes disease) is an important precursor to osteoarthritis. Clinical radiographs are often used to characterize deformity and provide indication for surgical correction, but it is unclear whether radiographs can adequately describe three-dimensional deformity. Statistical shape modeling, a method of describing a population of shapes using a small number of variables, has been identified as a potential tool that will allow clinicians and researchers to validate current and novel radiographic measurements of hip deformity. In identifying all previous examples of statistical shape modeling applied to the hip joint, this review will determine its prevalence, strengths, and weaknesses, and identify gaps in the literature. INCLUSION CRITERIA: Peer-reviewed and gray literature focusing on the development and/or application of statistical shape models to the human hip joint will be included. METHODS: Several relevant databases, including Ovid MEDLINE, Embase, and IEEE, will be searched for literature published from 1992, and for a title and abstract that can be searched in English. After removal of duplicates, two reviewers will independently screen papers by title and abstract, then screen the full text of selected or uncertain papers. The same reviewers will then independently chart data from the final selection. At each stage, disagreements will be resolved through discussion or third-party arbitration.
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 | 0.152 | 0.149 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".