Predictors of functional outcomes following limb salvage surgery for lower-extremity soft tissue sarcoma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patient function has been conceptualized by clinical measures such as joint motion, muscle strength, disability, and general health status. The purpose of the current study was to evaluate tumor and treatment variables predictive of these conceptually different posttreatment functional outcomes in patients treated with limb preservation surgery for lower-extremity soft tissue sarcoma. METHODS: One hundred seventy-two patients with minimum 1-year follow-up were evaluated using the following outcomes: impairment, measured by the 1987 and 1993 versions of the Musculoskeletal Tumor Society Rating Scale (MSTS); disability, measured by the Toronto Extremity Salvage Score (TESS); and general health status, using the Short Form-36 (SF-36). Tumor and treatment-related variables (age, gender, presenting disease status, anatomic site, tumor size, grade, depth, prior excision, irradiation, bone resection, motor nerve sacrifice, and complications) were extracted from the STS database. RESULTS: Large tumor size, bone resection, motor nerve resection, and complications were predictive of lower MSTS 1987 and 1993 scores. Patients with large, high-grade tumors who required motor nerve resection were more disabled, as reflected by lower TESS scores. Only age and prior surgery were adverse predictors of SF-36 score. CONCLUSIONS: These results demonstrate that different factors are predictive of different patient outcomes, specifically, impairment, disability, and general health status. It is important to define function when counseling patients regarding their potential recovery based on tumor and treatment-related variables. J. Surg. Oncol. 2000;73:206-211.
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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 | 0.004 |
| 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 | 0.000 | 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".