Psychological Problems among Head and Neck Cancer Patients in Relation to Utilization of Healthcare and Informal Care and Costs in the First Two Years after Diagnosis
Bibliographic record
Abstract
BACKGROUND: To investigate associations between psychological problems and the use of healthcare and informal care and total costs among head and neck cancer (HNC) patients. METHOD: Data were used of the NETherlands QUality of Life and Biomedical Cohort study. Anxiety and depression disorder (diagnostic interview), distress, symptoms of anxiety and depression (HADS), and fear of cancer recurrence (FCR) and cancer worry scale (CWS) were measured at baseline and at 12-month follow-up. Care use and costs (questionnaire) were measured at baseline, 3-, 6-, 12-, and 24-month follow-up. Associations between psychological problems and care use/costs were investigated using logistic and multiple regression analyses. RESULTS: Data of 558 patients were used. Distress, symptoms of anxiety or depression, FCR, and/or anxiety disorder at baseline were significantly associated with higher use of primary care, supportive care, and/or informal care (odds ratios (ORs) between 1.55 and 4.76). Symptoms of anxiety, FCR, and/or depression disorder at 12-month follow-up were significantly associated with use of primary care, supportive care, and/or informal care (ORs between 1.74 and 6.42). Distress, symptoms of anxiety, and FCR at baseline were associated with higher total costs. DISCUSSION: HNC patients with psychological problems make more use of healthcare and informal care and have higher costs. This is not the result of worse clinical outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".