Clinical outcomes of total femoral replacement. First Latin American experience
Bibliographic record
Abstract
INTRODUCTION: The femur is frequently affected by primary and metastatic bone tumors. In cases with substantial bone loss, Total Femur Replacement (TFR) remains the only viable limb preservation option. This study investigates the clinical outcomes of TFR patients in a Latin American setting, with a minimum 3-year follow-up. METHODS: Retrospective review identifying cases of TFR at a single center from 2009 to 2020. Patients who had TFR either due to oncological indications or complications arising from oncology-related surgeries were included. Data on the indications for surgery and post-operative complications were recorded. To assess functional status, the Musculoskeletal Tumor Society (MSTS) score and the Toronto Extremity Salvage Score (TESS) were used. RESULTS: Fourteen patients met the inclusion criteria. Diagnoses included eight osteosarcomas, four chondrosarcomas, one Ewing sarcoma, and one giant cell tumor. Ten patients had undergone prior surgeries. Indication for TFR was a complication of a previous surgery in 78.6 % of cases. Post-TFR complications were experienced by 35.7 % of patients, requiring further surgeries. At the 3-year mark, average MSTS and TESS scores were 67.4 % and 70.8 %, respectively. CONCLUSION: Total femur replacement serves as a valuable limb salvage solution for patients with significant femoral defects in oncological scenarios, however, there is a significant risk of complications. Given its potential benefits, it is essential for developing countries to consider incorporating TFR into their healthcare systems.
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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 | 0.001 |
| 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) | 0.002 | 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".