The Edmonton Symptom Assessment System as a Screening Tool for Depression and Anxiety
Bibliographic record
Abstract
PURPOSE: Mood disorders are among the most important psychiatric problems in patients with cancer. However, they are frequently underdiagnosed and therefore undertreated. This may lead to difficulties with symptom control, social withdrawal, and poor quality of life. This study was conducted to evaluate the screening performance of the Edmonton Symptom Assessment System (ESAS) for depression and anxiety, compared to Hospital Anxiety and Depression Scale (HADS). METHODS: We retrospectively reviewed and analyzed ESAS and HADS data collected from three previous clinical trials conducted by our group. The diagnosis of depression and/or anxiety, and moderate/severe depression and/or anxiety made when patients scored 8 or more, and 11 or more in HADS questionnaire, respectively. The sensitivity, specificity, positive, and negative predictive values for ESAS were calculated. RESULTS: Of 216 patients analyzed, the median (range) score for depression was 2 (0-10) and anxiety 3 (0-10) using ESAS, and 6 (0-16) and 7 (0-19) using HADS, respectively. A cut off of 2 out of 10 or more in the ESAS gave a sensitivity of 77% and 83% with a specificity of 55% and 47% for depression and moderate/severe depression, respectively. A cutoff of 2 out of 10 or more in the ESAS gave a sensitivity of 86% and 97%, and a specificity of 56% and 43% for anxiety and moderate/severe anxiety, respectively. CONCLUSION: Our data suggest that the ideal cutoff point of ESAS for the screening of depression and anxiety in palliative care is 2 out of 10 or more. More research is needed to define the ideal cutoff point for screening of severe depression and anxiety.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".