Preliminary Reliability and Validity Testing of a New Skin Toxicity Assessment Tool (STAT) in Breast Cancer Patients Undergoing Radiotherapy
Bibliographic record
Abstract
Clear consensus on the clinical evaluation of acute skin toxicity among cancer patients undergoing radical radiotherapy (RT) is currently lacking. This study investigates the reliability and validity of a new Skin Toxicity Assessment Tool (STAT) to evaluate the objective and subjective manifestations of RT-induced acute skin effects. The STAT was designed by a multidisciplinary team involved in the management of radiation skin reactions. The tool has 3 components: patient and treatment parameters, observer scoring, and patient-reported symptoms, and was piloted in a cohort of 27 breast cancer patients by pairs of independent blinded observers. Each patient was assessed weekly during RT and 2 weeks after therapy completion. Validity and reliability testing of the STAT was performed. Information on the tool's ease of use was obtained by recording the time necessary to complete the assessment at each visit and by a survey among the tool's users. All subjects developed some degree of skin reaction during breast RT. The level of agreement between observers in eliciting subjective complaints ranged from 72% to 92% (95% CI = 63-96%; kappa = 0.33-0.68). The interobserver agreement in scoring skin reactions ranged from 65.0 to 97.5% (kappa = 0.46-0.81). Objective and subjective toxicity scores were significantly correlated (P < 0.05). The STAT was easy to use and required on average a few minutes to complete at each visit. The STAT is an easy-to-use, standardized instrument to evaluate acute skin reaction and may be applied to clinical care and research in patients undergoing radiotherapy.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".