A more accurate method to determine the magnification of radiographs when templating for hip arthroplasty?
Bibliographic record
Abstract
The use of digital templating for Total Hip Arthroplasty (THA) is now the standard approach for pre-operative planning. Digital templating holds potential to reduce operative time and post-op complications however, this often relies on imprecise assumptions. The relationship between the X-ray source, subject and detector alters the perceived magnification. We therefore determine if Body Mass Index (BMI) is positively correlated with true magnification and if a predictive model based these parameters exists. A single surgeon series (n=107) was included in this study. Two independent observers assessed both pre- and post-operative AP pelvis radiographs using TraumaCad™. Post-operative radiographs were assessed to calculate the true magnification by calibrating from a known femoral head prosthesis size. Finally, a scatter plot with regression was used to determine if a predictive model of magnification existed using the Body Mass Index. The mean pre-operative magnification using a scaling marker was 124.2 ± 8.90%. The mean post-operative magnification using a known femoral head prosthesis size (true magnification) was 123.7 ± 3.98%. Significant variability exists in pre-operative marker data. Regression modelling showed no significant correlation between BMI and true magnification (post-op magnification). This study’s suggests that the precision and reliability of the radiographic marker in daily practice is poor. Regression modelling showed no significant correlation between BMI and the true magnification factor. Therefore, a pre-op predictive model cannot be reliably used. The data from this study suggest that a fixed magnification factor of 124% remains the most reliable and accurate method.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".