Patient‐reported outcomes in childhood head and neck rhabdomyosarcoma survivors and their relation to physician‐graded adverse events—A multicenter study using the <scp>FACE‐Q</scp> Craniofacial module
Bibliographic record
Abstract
INTRODUCTION: Adverse events (AE) of treatment are prevalent and diverse in head and neck rhabdomyosarcoma (HNRMS) survivors. These AEs are often reported by physicians; however, patients' perceptions of specific AE are not well known. In this study, we explored patient-reported outcomes measuring appearance, health-related quality of life (HRQOL), and facial function in HNRMS survivors. Second, we assess the relationship between physician grading of AE and patient reporting. MATERIALS AND METHODS: Survivors of pediatric HNRMS, diagnosed between 1993 and 2017, who were at least 2 years after completing treatment were invited to an outpatient clinic as part of a multicenter cross-sectional cohort study. At the outpatient clinics, survivors aged ≥8 years filled out the FACE-Q Craniofacial module; a patient-reported outcome instrument measuring issues specific to patients with facial differences. AE were systematically assessed by a multidisciplinary team based on the Common Terminology Criteria of Adverse Events system. RESULTS: Seventy-seven survivors with a median age of 16 years (range 8-43) and median follow-up of 10 years (range 2-42) completed the questionnaire and were screened for AEs. Patient-reported outcomes varied widely between survivors. Many survivors reported negative consequences: 82% on appearance items, 81% on HRQOL items, and 38% on facial function items. There was a weak correlation between physician-scored AEs and the majority of patient-reported outcomes specific for those AEs. CONCLUSIONS: Physician-graded AEs are not sufficient to provide tailored care for HNMRS survivors. Findings from this study highlight the importance of incorporating patient-reported outcome measures in survivorship follow-up.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".