Patient factors affecting the Toronto extremity salvage score following limb salvage surgery for bone and soft tissue tumors
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The Toronto extremity salvage score (TESS) assesses physical function following limb salvage for bone and soft tissue sarcoma. In 2012, Clayer et al. showed increasing age affects the TESS score in normal individuals. The purpose of this study was to investigate what other patient factors affect outcome? METHODS: We reviewed the TESS scores, age, sex, BMI, diagnosis, smoking status, and social deprivation score of patients who have undergone limb salvage in our unit. Data were extracted from our tumor database and reviewed. Statistical analysis was performed using Wilcoxon pairwise test and linear regression analysis. RESULTS: Four hundred and ninety-eight TESS scores were found for 198 patients. Data were analyzed separating upper limb (UL) and lower limb (LL) tumors. In the UL group, being female (P = 0.01) and having a bone lesion (P < 0.001) were associated with a lower TESS score. In the LL group, being female (P = 0.04), increasing age (P = 0.002), having a bone lesion (P < 0.001), increasing BMI (P < 0.001), and smoking (P = 0.005) were associated with a lower TESS score. CONCLUSIONS: Analysis has shown that female sex, increasing age and BMI, smoking and having a bone lesion have an adverse effect on physical function following limb salvage, as indicated by the mean TESS score. J. Surg. Oncol. 2016;113:804-810. © 2016 Wiley Periodicals, Inc.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".