Gaps in Depression Symptom Management for Patients With Head and Neck Cancer
Bibliographic record
Abstract
OBJECTIVE: To understand practice patterns and identify care gaps within a large-scale depression screening program for patients with head and neck cancer (HNC). STUDY DESIGN: Retrospective cohort study. METHODS: This was a population-based study of adults diagnosed with a HNC between January 2007 and October 2020. Each patient was observed from time of first symptom assessment until end of study date, or death. The exposure of interest was a positive depressive symptom screen on the Edmonton Symptom Assessment System (ESAS). Outcomes of interest included psychiatry/psychology assessment, social work referral, or palliative care assessment. Cause specific hazard models with a time-varying exposure were used to investigate the exposure-outcome relationships. RESULTS: Of 14,054 patients with HNC, 9016 (64.2%) reported depressive symptoms on at least one ESAS assessment. Within 60 days of first reporting depressive symptoms, 223 (2.7%) received a psychiatry assessment, 646 (7.9%) a social work referral, and 1131 (13.9%) a palliative care assessment. Rates of psychiatry/psychology assessment (HR 3.15 [95% CI 2.67-3.72]), social work referral (HR 1.83 [95% CI 1.64-2.02]), and palliative care assessment (HR 2.34 [95% CI 2.19-2.50]) were higher for those screening positive for depression. Certain patient populations were less likely to receive an assessment including the elderly, rural residents, and those without a prior psychiatric history. CONCLUSION: A high proportion of head and neck patients report depressive symptoms, though this triggers a referral in a small number of cases. These data highlight areas for improvement in depression screening care pathways. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:2638-2646, 2023.
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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".