Screening for depression in head and neck cancer
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study are to identify the prevalence of depression and the accuracy of depression screening instruments in ambulatory head and neck cancer patients who have received radiation. This population is at risk for depression because of the life-threatening nature of the illness, and treatment-induced oral morbidity. METHODS: Sixty subjects were evaluated for Major and Minor Depression according to Research Diagnostic Criteria using the Schedule for Affective Disorders and Schizophrenia (SADS). Screening instruments examined were the Beck Depression Inventory (BDI), the Hospital Anxiety and Depression Scale (HADS) and the Centre for Epidemiological Studies-Depression (CES-D) scale. Accuracy was assessed by calculating the sensitivities, specificities, positive predictive values (PPV) and areas under curve (AUC) from Receiver Operating Characteristic (ROC) curves. RESULTS: The prevalence of Major and Minor Depression was 20%. All of the screening instruments tested were found to be highly accurate. Statistically significant differences between the instruments were not observed but the HADS demonstrated the highest absolute levels of sensitivity, specificity and PPV. No cases of Major Depression were missed by any of the instruments tested. CONCLUSIONS: These results suggest that a significant minority of head and neck cancer patients are depressed in the post radiation period, and that accurate screening for clinically significant depression is possible using any of the three instruments evaluated here.
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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 | 0.000 |
| 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) | 0.001 | 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".