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Skin Elasticity as a Measure of Radiation Fibrosis: Is it Reproducible and Does it Correlate with Patient and Physician-reported Measures?

2013· article· en· W2058741651 on OpenAlexaffabout
Nhu-Tram A. Nguyen

Bibliographic record

VenueTCRT Express · 2013
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsJuravinski Cancer Centre
Fundersnot available
KeywordsMedicineConcordanceConcordance correlation coefficientGrading scaleGrading (engineering)FibrosisSurgeryInternal medicineMathematicsStatistics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.238
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2013
Admission routes2
Has abstractyes

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