Prevalence and Risk Factors of Suicidal Ideation among Patients with Head and Neck Cancer: Longitudinal Study
Bibliographic record
Abstract
OBJECTIVES: (1) Determine 1-year period prevalence of suicidal ideation, suicide attempt, and completed suicide among patients newly diagnosed with a first occurrence of head and neck cancer (HNC). (2) Characterize stability and trajectory of suicidal ideation over the year following cancer diagnosis. (3) Identify patients at risk of suicidal ideation. STUDY DESIGN: Prospective longitudinal study with 1-year follow-up. SETTING: Three university-affiliated outpatient departments of otolaryngology-head and neck surgery. SUBJECTS AND METHODS: The study comprised a representative sample of 223 consecutive patients who were newly diagnosed (<2 weeks) with a first occurrence of primary HNC, were ≥18 years old and able to consent, and had a Karnofsky Performance Scale score ≥60. Patients completed the Beck Scale for Suicidal Ideation and Structured Clinical Interview for DSM-IV-TR Axis I Disorders. RESULTS: Sixteen percent (15.7%) of patients with HNC were suicidal <1 year from diagnosis, with point prevalences of 8.1% <2 weeks, 14.8% at 3 months, 9.4% at 6 months, and 10.4% at 12 months; 0.4% committed suicide within 3 months, and 0.9% attempted suicide. An a priori comprehensive conceptual model revealed 2 predictors of 1-year period prevalence of suicidal ideation in HNC: psychiatric history ( P = .017, β = 2.1, 95% CI = 0.4-3.8) and coping with the diagnosis by using substances (alcohol/drugs; P = .008, β = 0.61, 95% CI = 0.16-1.06). All other predictors, including medical predictors, were nonsignificant. A clinical suicide risk assessment revealed low risk among 71.4% and medium to high risk among 28.6%. CONCLUSION: Suicide prevention strategies are clearly needed as part of routine clinical care in head and neck oncology, as well as their integration into clinical practice guidelines for HNC.
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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 | 0.000 |
| 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".