Functional outcome of a patient after hip disarticulation due to an infection 10 years after limb salvage surgery for osteosarcoma: A case report
Bibliographic record
Abstract
Limb salvage is a common procedure after extensive osteosarcoma resection. However, the long-term outcomes after limb salvage surgery (LSS) remain unclear. In this article, the case of a 24-year-old man who underwent hip disarticulation (HD) after an uncontrollable infection is presented. He was previously diagnosed with right distal femoral osteosarcoma and underwent LSS 10 years before disarticulation. Four years after LSS, an uncontrollable infection developed around the endoprosthesis, which eventually prompted HD. The Musculoskeletal Tumor Society (MSTS) functional rating system and the Toronto Extremity Salvage Score were used to compare the subject's activity statuses after LSS and HD. MSTS functional scores were 53.3% after LSS and 60% after HD. Toronto Extremity Salvage Scores were 78.3% after LSS and 95.8% after HD. The subject's emotional acceptance was 3 for LSS and 5 for HD (0 = worst and 5 = best). Both the MSTS and Toronto Extremity Salvage Scores were greater after HD than after LSS. The subject's improved emotional acceptance of the affected limb after HD contributed to the improved functional assessment scores. Alleviation of pain and other disabilities associated with the infection may have contributed to the higher functional scores after the more recent HD surgery. Even if amputation is unavoidable because of complications, high psychological acceptance may prevent a decrease in patient mobility and quality of life after amputation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".