Pre-Operative Planning of Total Hip Arthroplasty on Dysplastic Acetabuli
Bibliographic record
Abstract
BACKGROUND: When performing total hip arthroplasty (THA) on a dysplastic hip, proper positioning of the acetabular component may not allow for more than 70% coverage. Structural support in the form of an autograft or a high porosity metal augment may be necessary. The purpose of the study was to investigate the value of preoperative templating and deformity classification in predicting cup coverage and the need for structural support. METHODS: 65 cases of THA for DDH were retrospectively analysed. 2 observers independently classified each dysplastic hip according to Hartofilakidis and determined the extent of cup coverage via templating software on preoperative digital AP pelvic radiographs. RESULTS: Weighted kappa interobserver agreement was 0.68 for cup coverage and 0.76 for Hartofilakidis type. Structural support was necessary in 10 hips. No structural support was necessary in Hartofilakidis type 1, dysplasia cases. However, 27-30% of cases with type 2 or type 3 dysplasia required structural support. All cases with templated cup coverage of 65% or less required structural support. Templated coverage within 65-75% and over 75% resulted in 20% and 10% of patients receiving structural augmentation, respectively. CONCLUSIONS: Preoperative planning for THA in the setting of hip dysplasia is crucial and can provide valuable insight to the need for column augmentation. However, the 3-D severity of the deformity may be underestimated in the 2-D radiographs.
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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".