A screening algorithm for early detection of major depressive disorder in head and neck cancer patients post‐treatment: Longitudinal study
Bibliographic record
Abstract
OBJECTIVE: The primary purpose of this study was to identify predictors of Major Depressive Disorder in head and neck cancer (HNC) patients in the immediate post-treatment period (ie, at 3 months post-diagnosis), with a focus on previously unexamined historical and contextual factors. METHODS: Prospective longitudinal study of 223 consecutive adults (72% participation) newly diagnosed with a first occurrence of primary HNC, including validated psychometric measures, Structured Clinical Interviews for DSM Disorders, and medical chart reviews. RESULTS: The 3-month period prevalence of Major Depressive Disorder was 20.4%; with point prevalences of 6.8% upon HNC diagnosis, 14.2% at 3 months, and 22.6% lifetime. Patients most susceptible to developing Major Depressive Disorder in the immediate post-treatment period: were diagnosed with advanced-stage cancer rather than early-stage cancer (O.R. = 4.94, P = 0.04), received surgery only (O.R. = 8.73, P = 0.04), presented a lifetime history of Anxiety Disorder on SCID-I (O.R. = 6.62; P = 0.01), and indicated higher pre-treatment levels of anxiety on the HADS (O.R. = 0.45, P = 0.05). CONCLUSIONS: Our results outline the predominant role of anxiety upon diagnosis as a precursor to post-treatment Major Depressive Disorder, suggesting the need for identification and prophylactic treatment of anxiety upon diagnosis in head and neck cancer patients. Further investigation into pathways by which pre-treatment anxiety predisposes to post-treatment Major Depressive Disorder in this population is warranted.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".