Prediction of pathologic fracture risk of the femur after combined modality treatment of soft tissue sarcoma of the thigh
Bibliographic record
Abstract
BACKGROUND: The objective of the current study was to formulate a scoring system to enable decision making for prophylactic stabilization of the femur after surgical resection of a soft tissue sarcoma (STS) of the thigh. METHODS: A logistic regression model was developed using patient variables collected from a prospectively collected database. The study group included 22 patients who developed a radiation-related pathological fracture of the femur after surgery and radiotherapy for an STS of the thigh. The control group of 79 patients received similar treatment but did not sustain a fracture. No patients received chemotherapy. The mean follow-up was 8.6 years. The variables examined were age, gender, tumor size, radiation dose (low [50 grays (Gy)] vs high [> or = 60 Gy]), extent of periosteal stripping (<10 cm, 10-20 cm, and >20 cm), and thigh compartment involvement (posterior, adductor, anterior or other [ie, abductors and groin]). RESULTS: On the basis of an optimal regression model, the ability to predict radiation-associated fracture risk was 91% sensitive and 81% specific. The area under the receiver operating characteristic curve was 0.9, which supports this model as a very accurate predictor of fracture risk. CONCLUSIONS: Radiation-related fractures of the femur after combined surgery and radiotherapy for STS are uncommon, but are difficult to manage and their nonunion rate is extremely high. The results of the current study suggest that it is possible to predict radiation-associated pathological fracture risk using patient and treatment variables with high sensitivity and specificity. This would allow for the identification of high-risk patients and treatment with either close follow-up or prophylactic intramedullary nail stabilization. The presentation of this model as a nomogram will facilitate its clinical use.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".