ANATOMICAL INFLUENCES ON FUNCTIONAL OUTCOME IN LOWER EXTREMITY SOFT TISSUE SARCOMA.
Bibliographic record
Abstract
Aim: To explore the relationship between anatomical location in lower extremity soft tissue sarcoma and function as measured by the Musculoskeletal Tumour Society (MSTS 93) rating and Toronto Extremity Salvage Score (TESS). Methods: 207 patients of median age 54 years (15 to 89) were reviewed. 58 tumours were superficial and 149 deep. Deep tumours were allocated to one of 9 locations based on anatomical compartments. Results: Treatment of superficial tumours did not lead to significant changes in MSTS (mean 90.6% vs 93.0%, p=0.566) or TESS (mean 86.4% vs 90.9%, p=0.059). Treatment of deep tumours lead to significant reductions in MSTS and TESS (mean 86.9% vs. 83.0%, p=0.001. mean 83.0% vs. 79.4%, p=0.015). There were no significant differences in MSTS and TESS when overall scores were compared by anatomical location. Exploratory analysis of MSTS subscales showed groin tumours were more painful than others, and posterior calf tumours had the lowest scores for gait. TESS subscales analysis suggested groin and buttock tumours were associated with difficulty sitting, and groin tumours were associated with difficulty dressing. Further exploratory analysis suggested “conservative” surgical excision of low-grade liposarcomas in all locations was associated with a significant decrease in functional scores. Conclusion: There is significant variation in MSTS and TESS subscale scores when anatomical locations are compared. The “conservative” surgery used in the treatment of low-grade fatty tumours in all locations has a significant impact on functional scores.
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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 | 0.002 |
| 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) | 0.002 | 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".