Acetabular reconstruction with an ice‐cream cone prosthesis following resection of pelvic tumors: Does computer navigation improve surgical outcome?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Acetabular reconstruction with a coned-stem prosthesis has been one of the reliable procedures following pelvic tumor resections but is associated with a risk of complications and postoperative morbidity. We investigated whether navigated reconstruction could decrease the complication rate and optimize outcomes. METHODS: A retrospective study was conducted on 33 patients who underwent acetabular resection and reconstruction with ice-cream cone prostheses; outcomes were compared between the navigated and nonnavigated groups. RESULTS: A clear margin was obtained in 91% and 82% of the navigated and nonnavigated groups, respectively. The local recurrence (LR) rate was 12%, and all LRs occurred in the nonnavigated group. The rate of major complications requiring surgical intervention was significantly lower in the navigated group (9%) than in the nonnavigated group (50%; P = .024). Two implant failures occurred in the nonnavigated group. Functional outcomes were significantly correlated with the occurrence of major complications (P = .010) and the use of navigation (P = .043); superior functional scores were observed in the navigated group (Musculoskeletal Tumor Society, 73% vs 55%; Toronto Extremity Salvage Score, 73% vs 56%). CONCLUSION: Ice-cream cone prosthesis is an acceptable reconstruction modality following periacetabular tumor resections, and computer navigation are useful to facilitate proper resection margins and implant position.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".