Acetate Templating on Digital Images Is More Accurate Than Computer-based Templating for Total Hip Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Templating is an important aspect of preoperative planning for total hip arthroplasty and can help determine the size and positioning of the prosthesis. Historically, templating has been performed using acetate templates over printed radiographs. As a result of the increasing use of digital imaging, surgeons now either obtain additional printed radiographs solely for templating purposes or use specialized digital templating software, both of which carry additional cost. QUESTIONS/PURPOSES: The purposes of this study was to compare acetate templating of digitally calibrated images on an LCD monitor to digital templating in terms of (1) accuracy; (2) reproducibility; and (3) time efficiency. METHODS: Acetate onlay templating was performed directly over digital radiographs on an LCD monitor and was compared with digital templating. Five separate observers participated in this study templating on 52 total hip arthroplasties. For the acetate templating, the digital images were magnified to the scaled reference on the templates provided by the manufacturer (ratio 1.2:1) before templating using a 25-mm marker as a reference. Both the acetate and digital templating results were then compared with the actual implanted components to determine accuracy. Interobserver and intraobserver variability was determined by an intraclass correlation coefficient. Observers recorded time to complete templating from the time of complete upload of patients' imaging onto the system to completion of templating. RESULTS: Both acetate and digital templates demonstrated moderate accuracy in predicting within one size of the eventual implanted acetabular cup (77% [199 of 260]; 70% [181 of 260], respectively; p = 0.050; 95% confidence interval [CI], 0.058-0.32), whereas acetate templating was better at predicting the femoral stem compared to digital templating (75% [195 of 260]; 60% [155 of 260], respectively; p < 0.001; 95% CI, 0.084-0.32). Acetate templating showed moderate to substantial interobserver agreement (cup intraclass correlation coefficient [ICC] = 0.55; 95% CI, 0.14-0.86; femoral ICC = 0.75; 95% CI, 0.39-0.95) and both methods showed almost perfect intraobserver agreement in reproducibility (acetate cup ICC = 0.82; 95% CI, 0.66-0.97; acetate femoral ICC = 0.86; 95% CI, 0.74-0.97; digital cup ICC = 0.82; 95% CI, 0.68-0.97; digital femoral ICC = 0.88; 95% CI, 0.77-1.0). Acetate templating could be performed more quickly (acetate mean 119 seconds; range, 37-220 seconds versus 154 seconds; range, 73-343 seconds; p < 0.001). CONCLUSIONS: Acetate onlay templating on digitally calibrated images can be a reliable substitute for digital templating using specialized software. It is quicker to perform and much less expensive. Hospitals and practices need not purchase expensive software, particularly at lower volume centers. LEVEL OF EVIDENCE: Level III, diagnostic study.
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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 | 0.005 | 0.026 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".