The utility of the Edmonton Symptom Assessment System in screening for anxiety and depression
Bibliographic record
Abstract
The Edmonton Symptom Assessment System (ESAS) is a common screening tool in cancer, although its validity for distress screening is unproven. Here, screening performance of the ESAS anxiety (ESAS-A) and depression (ESAS-D) items were validated against the anxiety [Generalised Anxiety Disorder-7 (GAD-7)] and depression [Patient Health Questionnaire-9 (PHQ-9)] subscales of the PHQ. A total of 1215 cancer patients completed the Distress Assessment and Response Tool (DART), a computerised distress screening instrument. Spearman's rank correlation coefficients and receiver operating characteristic curve analyses were used to evaluate the ability of ESAS-A and ESAS-D to identify moderate distress (GAD-7/PHQ-9 ≥ 10). Spearman's rank correlation coefficients comparing ESAS-A and ESAS-D with GAD-7 and PHQ-9 were 0.74 and 0.72 respectively. Areas under the receiver operating characteristic curves were 0.89 and 0.88 for anxiety and depression respectively. A cut-off of ≥3 on ESAS-A demonstrated a sensitivity of 0.91, specificity of 0.68, positive predictive value of 0.34 and negative predictive value of 0.97. A cut-off of ≥2 on the ESAS-D demonstrated a sensitivity of 0.86, specificity of 0.72, positive predictive value of 0.46 and negative predictive value of 0.95. High sensitivities of ESAS-A and ESAS-D at certain cut-offs suggest they have use in ruling-out distress. However, their low specificities indicate secondary screening is needed to rule-in anxiety or depression for case-finding.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".