Preliminary Reliability and Validity Testing of a New Skin Toxicity Assessment Tool (STAT) in Breast Cancer Patients Undergoing Radiotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it