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.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".