Clinical and functional outcomes of patients with a pathologic fracture in high‐grade osteosarcoma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There have been variable reports of outcomes of patients with osteosarcoma and pathologic fractures. The purpose of this study was to document outcomes after management of this clinical entity at a single large oncology center. METHODS: A retrospective review was undertaken of our database between 1989 and 2006. We compared oncologic and functional outcomes of 201 patients with high-grade osteosarcoma without pathologic fractures to 31 patients with pathologic fractures. RESULTS: The rate of amputation in the group with pathologic fracture was significantly higher than the group without fracture (39% vs. 14%, P = 0.001). There was no difference in the rate of local recurrence between groups. The 5-year survival was superior in the group without pathologic fracture (60% vs. 41%, P = 0.0015). For patients with localized disease, 5-year survival was higher in patients without fracture (68% vs. 52%, P = 0.006). Disability as measured by the Toronto Extremity Salvage Score was no different between the groups. Impairment as measured by the Musculoskeletal Tumor Society scores was lower in the group without fracture. CONCLUSIONS: Presentation with a pathologic fracture in osteosarcoma did not preclude limb salvage surgery in a majority of patients, did not increase the risk of local recurrence, but was associated with poorer overall survival.
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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 | 0.003 |
| 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) | 0.001 | 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".