Predicting Depression and Quality of Life among Long‐term Head and Neck Cancer Survivors
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to identify clinical factors that are predictive of depression and quality of life (QOL) among long-term survivors of head and neck squamous cell carcinoma and to develop predictive scores using these factors. STUDY DESIGN: Cohort study SETTING: Tertiary referral center. SUBJECTS AND METHODS: A total of 209 posttreatment (median follow-up, 38.7 months) head and neck cancer patients were prospectively evaluated using the Hospital Anxiety Depression Scale (HADS), the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30, and the EORTC Quality of Life Questionnaire Head and Neck 35, and pretreatment patient-related, tumor-related, and treatment-related predictors were identified using chart review. Bivariate (χ(2) and t test) and multivariate (linear regression) analyses were used to construct predictive models. RESULTS: Significant pretreatment predictors of depression were identified on multivariate analysis as smoking at diagnosis, >14 alcoholic drinks per week, T3 or T4 status, and >3 medications (P < .001). Two or more of these factors yielded an 82.3% sensitivity in detecting significant depressive symptoms (defined as a HADS cutoff score of 5). Significant predictors of fatigue, global health/QOL, social contact, speech, pain, swallowing, and xerostomia were also identified. CONCLUSION: Pretreatment predictors of long-term depression and QOL have been defined using multivariate models, and an easily applicable predictive score of long-term depression is proposed. Potential eventual clinical applications include prophylactic intervention in at-risk patients.
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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".