Contextual and historical factors for increased levels of anxiety and depression in patients with head and neck cancer: A prospective longitudinal study
Bibliographic record
Abstract
BACKGROUND: This study aimed at examining predictors of clinical anxiety and depressive symptoms in patients with head and neck cancer (HNC) at 3, 6, and 12 months post-diagnosis, with a particular interest in contextual and historical factors. METHODS: Prospective longitudinal study of 219 consecutive patients newly diagnosed with a first occurrence of primary HNC, including psychometric measures, Structured Clinical Interview for DSM-IV Diagnoses (SCID), and medical chart reviews. RESULTS: Point prevalence of clinical anxiety symptoms (Hospital Anxiety and Depression Scale-Anxiety subscale) was 32.0%, 21.9%, 12.1%, and 12.6% at baseline, 3, 6, and 12 months; and clinical depressive symptoms on the Depression Subscale was 19.4%, 21.9%, 13.5%, and 9.2%, respectively. Predictors of anxiety and depressive symptoms included upon diagnosis SCID major depressive or anxiety disorder, stressful life events in previous year, neuroticism, and levels of anxiety and depressive symptoms upon cancer diagnosis. CONCLUSIONS: This study emphasizes the predictive contribution of broader personal contextual and historical factors that increase psychological vulnerability in HNC and merit consideration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".