Automated digital templating of component sizing is accurate in robotic total hip arthroplasty when compared to predicate software
Bibliographic record
Abstract
Accurate pre-operative templating of prosthesis components is an essential factor in successful total hip arthroplasty (THA), including robotically-assisted THA (RA-THA) techniques. We sought to validate the accuracy of a novel, robotic-optimized THA planning software compared to a predicate THA planner for component sizing. We analyzed a series of 199 patients who received manual THA (mTHA) and fluoroscopy-based RA-THA at a single institution. All cases were templated using a predicate pre-operative templating software. For RA-THA cases, the novel robotic-optimized pre-operative planner software was also used for templating. The differences between templated and implanted acetabular cup, femoral head, and stem component sizes were compared based on matching within 1, 2, and ≥3 sizes. Differences in templated and implanted femoral stem implant geometry were also compared. The robot-optimized pre-operative RA-THA plans demonstrated equivalent accuracy to that of predicate pre-operative plans for both RA-THA and mTHA cases. Templated acetabular cups (90.4 vs. 86.8 vs. 82.8; p = 0.421), femoral stems (76.0 vs. 65.1 vs. 67.7; p = 0.096), and femoral heads (91.3 vs. 96.2 vs. 88.2; p = 0.302) were within +/-1 size of implanted components. No significant differences were detected in the proportion of matching templated and implanted stem geometry across the study cohorts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".