Functional evaluation for patients with lower extremity sarcoma: application of the Chinese version of Musculoskeletal Tumor Society scoring system
Bibliographic record
Abstract
BACKGROUND: The Musculoskeletal Tumor Society (MSTS) scoring system is a disease-specific instrument to determine the physical and mental health for patients with extremity sarcoma. This study aims to investigate the reliability and validity of the Chinese version of the MSTS, and to evaluate functional outcomes of the surgical treatment of lower extremity sarcoma using the Chinese MSTS. METHODS: A cohort of 98 patients who had undergone surgery for lower extremity sarcoma were included. All the patients completed the clinical assessment with the Chinese MSTS and the Chinese Toronto Extremity Salvage Score (TESS). Assessment of psychometric properties was carried out through reliability and validity test. The reliability of Chinese MSTS was evaluated through test-retest analysis, inter-observer analysis and internal consistency. The inter-observer and test-retest reliability was analyzed with intra-class correlation coefficient (ICC). The internal consistency was evaluated by Cronbach's α, with a value >0.70 considered acceptable. The discriminant validity was evaluated through comparison of the MSTS score between patients undergoing amputation surgeries and those undergoing limb-salvage surgeries. The construct validity was evaluated with the factor analysis. RESULTS: The mean MSTS score was 21.5 ± 7.1. The ICC was 0.91 (95% confidence interval (CI) = 0.85-0.96) for the test-retest reliability and 0.90 (95% CI = 0.86-0.93) for the inter-observer analysis. The test for internal consistency showed a Cronbach's α of 0.86 for the MSTS. Patients undergoing amputation surgery had remarkably lower MSTS score than patients undergoing limb-salvage surgeries (18.8 ± 5.4 vs. 23.5 ± 6.3, p = 0.005), which indicated a good discrinimant validity of the Chinese MSTS. The factor analysis indicated a 1-factor model with acceptable goodness of fit. CONCLUSIONS: The Chinese MSTS scoring system is a reliable and valid instrument with well-accepted psychometric properties. Through application of the Chinese MSTS, we demonstrated that patients receiving limb-salvage surgeries may have better functional outcome and QoL than those undergoing amputation surgeries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".