Screening for Distress and Health Outcomes in Head and Neck Cancer
Bibliographic record
Abstract
Head and neck cancers (HNC) have higher rates of emotional distress than other cancer types and the general population. This paper compares the prevalence of emotional distress in HNC across various distress screening measures and examines whether significant distress or distress screening are associated with cancer-related survival. A retrospective observational cohort design was employed, with data collected from the Distress Assessment and Response Tool (DART) and linkages to administrative databases from 2010 to 2016. Descriptive and prevalence data were reported using multiple concurrently administered distress tools, including the Patient Health Questionaire-9 (PHQ-9), Generalized Anxiety Disorders-7 (GAD-7), Edmonton Symptom Assessment Scale-revised (ESAS-r), and MD Anderson Symptom Index-Head and Neck module (MDASI-HN). Across measures, 7.8 to 28.1% of the sample reported clinically significant emotional distress, with PHQ-9 and GAD-7 identifying lowest prevalence of moderate/severe distress, and the ultrashort distress screens within ESAS-r and MDASI-HN performing equivalently. Cox hazards models were used in univariate and multivariate survival analyses. ESAS depression (≥4), but not anxiety, was associated with increased risk of cancer-related mortality and patient completion of DART was associated with greater cancer-related survival. The findings underscore the importance of implementing routine distress screening for HNC populations and the utility of ultra-brief screening measures.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".