DOES THE ADDITION OF CT IMPROVE THE ACCURACY OF DETERMINATION OF IMPLANT FIXATION STATUS FOR PERIPROSTHETIC FRACTURES OF THE PROXIMAL FEMUR?
Bibliographic record
Abstract
When treating periprosthetic femur fractures (PPFFs) around total hip arthroplasty (THA)], determining implant fixation status preoperatively is important, since this guides treatment regarding ORIF versus revision. The purpose of this study was to determine the accuracy of preoperative implant fixation status determination utilizing plain films and CT scans. Twenty-four patients who underwent surgery for Vancouver B type PPFF were included in the study. Two joint surgeons and two traumatologists reviewed plain films alone and made a judgment on fixation status. They then reviewed CT scans and fixation status was reassessed. Concordance and discordance were recorded. Interobserver reliability was assessed using Kendall's W and intraobserver reliability was assessed using Cohen's Kappa. Ultimately, the “correct” response was determined by intraoperative findings, as we routinely test the component intraoperatively. Fifteen implants were found to be well-fixed (63%) and 9 were loose. Plain radiographs alone predicted correct fixation status in 53% of cases. When adding the CT data, the correct prediction only improved to 55%. Interestingly, concordance between plain radiographs and CT was noted in 82%. In concordant cases, the fixation status was found to be correct in 55% of cases. Of the 18% of cases with discordance, plain films were correct in 43% of cases, and the CT was correct in 57%. Interobserver reliability demonstrated poor agreement on plain films and moderate agreement on CT. Intraobserver reliability demonstrated moderate agreement on both plain films and CT. The ability to determine fixation status for proximal PPFFs around uncemented femoral components remains challenging. The addition of routine CT scanning did not significantly improve accuracy. We recommend careful intraoperative testing of femoral component fixation with surgical dislocation if necessary, and the surgeon should be prepared to revise or fix the fracture based on those findings.
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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 | 0.001 | 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) | 0.000 | 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".