Three-Dimensional Analysis of the Cement Mantle in Total Hip Arthroplasty
Bibliographic record
Abstract
Cemented fixation of the femoral stem is the gold standard for patients older than 60 years. The importance of reliably achieving an adequate cement mantle has been shown in many studies. Currently, inspection and grading of plain radiographs is the accepted method for study of the cement mantle. However, the reliability of plain radiographs for this purpose has been questioned. In addition, the interobserver agreement of current grading systems has been shown to be limited. A new in vitro method of cement mantle analysis is described. Plastic replicas of six contemporary stems were implanted into femurs from cadavers. The specimens were imaged with a computed tomography scanner. Detailed, computer-assisted analysis of mantle thickness was done. Comparisons were made between designs. A subset was compared with standard radiographs. Plain radiographs overestimated thickness and underestimated the deficiencies. There was significant variability in the mantle produced by the different designs. Commonly used designs had deficiencies in their mantles by standard criteria despite proper surgical technique. The importance of being fully acquainted with the particular implant one uses is emphasized by these results. This is a valuable technique for investigation of the effects on the cement mantle of implant design, surgical technique, and patient anatomy.
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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 | 0.002 |
| 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 | 0.000 |
| 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 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".