Development and Validation of a Rapid Screening Measure for Mask Anxiety in Head and Neck Cancer Patients Undergoing Radiotherapy
Bibliographic record
Abstract
OBJECTIVE: This study aims to develop a brief screening questionnaire for early identification of radiotherapy mask-related anxiety in patients with head and neck cancer. METHODS: A cross-sectional study recruited 512 patients undergoing radiotherapy between April 2016 and February 2021. Participants completed a screening questionnaire assessing mask-related anxiety and were assessed for anxious response during CT or MRI planning scans. A subgroup completed the Claustrophobia Questionnaire (CLQ) for comparison. Data were analyzed using multivariate logistic regression and discriminant validity was evaluated using the area under the ROC curve (AUROC). RESULTS: The study developed the Radiotherapy Mask Anxiety Questionnaire (R-MAQ), consisting of four items, derived from the screening questionnaire, which showed good model fit (Hosmer-Lemeshow p = 0.92) and predictive accuracy (AUROC = 0.78). Of the 410 participants (mean age 62), 27.3% presented an anxious response to the immobilization mask during planning exams. The R-MAQ demonstrated comparable or superior validity to the CLQ and effectively identified patients at risk of radiotherapy mask-related anxiety. CONCLUSIONS: The R-MAQ is a valid and efficient tool for identifying patients at risk of mask-related anxiety during radiotherapy for head and neck cancer. It offers a clinically relevant, context-specific method for early intervention, potentially improving treatment adherence and outcomes. Future research should explore its broader applicability and impact on treatment efficacy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".