Two-Step Screening for Depression and Anxiety in Patients with Cancer: A Retrospective Validation Study Using Real-World Data
Bibliographic record
Abstract
Background: Although screening for distress is recommended by many cancer care guidelines, the uptake of such screening in cancer centers remains limited. Improving the acceptability of screening programs in cancer centers requires a reduction in clinical burden and an improved detection of distress. The purpose of this study was to validate the performance of the two-step screening algorithm used in the Distress Assessment and Response Tool (DART) for identifying cases of anxiety and depression. Methods: This retrospective validation study consisted of patients at the Princess Margaret Cancer Centre (PM) who completed the DART, which includes the Edmonton Symptom Assessment System depression (ESAS-D) and anxiety (ESAS-A) items, the Patient Health Questionnaire (PHQ-9), and the Generalized Anxiety Disorder (GAD-7). We evaluated the performance of a two-step screening approach, which modeled the ESAS-D, followed by the PHQ-9 and ESAS-A, then the GAD-7 for predicting a diagnosis of depression and anxiety disorders, respectively. A clinical psychiatric assessment was used as the gold standard reference. Results: A total of 172 patients with cancer were included in this study. A total of 59/172 (34%) and 39/172 (23%) were diagnosed with a depression or anxiety disorder, respectively. The sequential administration of the PHQ-9 ≥15 following the ESAS-D (>2) significantly increased the post-test probability of depression from 37% to 60% and improved the performance of predicting depression compared to both the ESAS-D or the PHQ-9 as standalone tests. The sequential administration of the GAD-7 after the ESAS-A did not improve the predictability of an anxiety diagnosis beyond the performance of the ESAS-A or the GAD-7 as standalone tests. Conclusions: The present study is among the first to demonstrate that a two-step screening algorithm for depression may improve depression screening in cancer using real-world data. Further research on optimal screening approaches for anxiety in cancer is warranted.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".