Skin Elasticity as a Measure of Radiation Fibrosis: Is it Reproducible and Does it Correlate with Patient and Physician-reported Measures?
Bibliographic record
Abstract
Current means of measuring RT-induced fibrosis are subjective. We evaluated the DermaLab suction cup system to measure objectively skin deflection as a surrogate for fibrosis. Sixty-nine patients with E-STS were treated with limb-sparing surgery and 50-66 Grays (Gy) of RT. Using a "scleroderma" DermaLab Suction Cup, the skin stiffness was measured by two clinicians. The National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE) scale, the Musculoskeletal Tumor Rating Scale (MSTS) and Toronto Extremity Salvage Score (TESS) questionnaires were completed for each patient. Levels of agreement between measurers were estimated using the Kappa (k) coefficient and the concordance correlation coefficient (CCC). All sixty-nine patients were included. The level of agreement between measurers for NCI-CTCAE grading was moderate (range k = 0.41-0.59). The CCC for the elasticity measurements were higher, with CCC = 0.82 for fibrotic skin and CCC 5 0.84 for normal skin. The elasticity measurements were significantly higher when MSTS scores were <30 and or TESS scores were <90. Suction Cup measurement of skin elasticity is more reproducible than CTCAE grading and shows promise in generating reproducible measurements for radiation-induced skin fibrosis. Furthermore, it correlates well with the MSTS and TESS.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".