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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".