Diagnostic Performance of Screening Tools for Depressive Symptoms in Vulnerable Older Patients with Cancer Undergoing Comprehensive Geriatric Assessment (CGA): Results from the SCREEN Pilot Study
Bibliographic record
Abstract
Depression is a common and disabling disorder in later life, particularly among people with poor physical health. There are many screening tools available that can be used to examine depressive symptoms; however, not all of them may be appropriate or accurate for older adults with cancer. This pilot study was designed to test the diagnostic performance of two screening tools and their short versions in a cohort of vulnerable (G8 score ≤ 14/17) older patients with cancer undergoing comprehensive geriatric assessment (CGA). The prospective analysis covered 50 vulnerable patients with cancer aged ≥70 years. The diagnostic performance of the Geriatric Depression Scale (GDS)-15, GDS-4, Patient Health Questionnaire (PHQ)-9 and PHQ-2 was compared to the 'gold standard' Structured Clinical Interview for DSM-5 Disorders (SCID-5-S) depression module A. The sensitivity and specificity in detecting depressive symptoms were the highest in the case of PHQ-2, with an area under the receiver operating characteristic curve (AUROC) of 92.7%. The AUROC for the 9-item version, PHQ-9, was 90.2%. For the GDS-15 and GDS-4, the AUROC was only 56.2% and 62.0%, respectively. The SCREEN pilot study illustrates the potential benefit of using a shorter screening tool, PHQ-2, to identify older patients with cancer who would benefit from a more in-depth emotional evaluation as part of a CGA.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, 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".