Accuracy of digital templating in uncemented primary total hip arthroplasty: which factors are associated with accuracy of preoperative planning?
Bibliographic record
Abstract
Background: Preoperative planning is a fundamental step for successful total hip arthroplasty (THA). Studies have highlighted the accuracy of preoperative digital templating for estimating acetabular cup and stem size. Stem design such as single-wedge metadiaphyseal (Type 1 stem) versus mid-short stem (microplasty) and surgical approach (anterior, direct lateral or posterior) have not been well investigated as predictors of THA templating accuracy. Methods: 204 patients (220 hips) who had undergone elective THA between November 2016 and December 2019 and presented a saved preoperative template were retrospectively reviewed. Templates from 5 different surgeons were involved in the analysis. 3 different approaches were used: direct lateral (DL), posterior (PA), direct anterior (DAA). 2 different stem designs were used: single-wedge metadiaphyseal and single-wedge mid-short (Biomet Taperloc Microplasty), while the acetabular component remained the same. Bivariate and multivariate regression analyses were performed to determine predictors of accuracy. Results: Femoral component size templating accuracy was significantly improved when using the single-wedge mid-short stem (Taperloc Microplasty) design when performing bivariate analysis. Although accuracy of cup sizing was not affected by approach, precision was significantly better in the PA group ( p < 0.05). Accuracy of templating was found to be independent of BMI and gender but dependent on presence of calibration marker and stem design ( p < 0.05). Conclusions: When striving for improved templating accuracy, acetabular and femoral component accuracy were best achieved using a calibration marker and a metaphyseal short femoral stem design.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".