Validation of a Patient-Reported Outcome Measure for Moist Desquamation among Breast Radiotherapy Patients
Bibliographic record
Abstract
There has been an increasing interest in patient-reported outcome (PRO) measures in both the clinical and research settings to improve the quality of life among patients and to identify when clinical intervention may be needed. The primary purpose of this prospective study was to validate an acute breast skin toxicity PRO measure across a broad sample of patient body types undergoing radiation therapy. Between August 2018 and September 2019, 134 women undergoing adjuvant breast radiotherapy (RT) consented to completing serial PRO measures both during and post-RT treatment and to having their skin assessed by trained trial radiation therapists. There was high patient compliance, with 124 patients (92.5%) returning to the clinic post-RT for at least one staff skin assessment. Rates of moist desquamation (MD) in the infra-mammary fold (IMF) by PRO were compared with skin assessments completed by trial radiation therapists. There was high sensitivity (86.5%) and good specificity (79.4%) between PRO and staff-reported presence of MD in the IMF, and there was a moderate correlation between the peak severity of the MD reported by PRO and assessed by staff (rho = 0.61, p < 0.001). This prospective study validates a new PRO measure to monitor the presence of MD in the IMF among women receiving breast RT.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".