The validity of the Edmonton Symptom Assessment System–Depression item for screening for depression in individuals with cancer pain: A cross-sectional study
Bibliographic record
Abstract
CONTEXT: Depression is common in individuals with cancer and pain, negatively impacts quality of life, treatment adherence, tumor progression, and survival. OBJECTIVES: The primary aims of this study were to (1) evaluate the validity of the Edmonton Symptom Assessment System's depression (ESAS-D) for detecting major depressive disorder (MDD) as diagnosed by a psychiatrist and (2) identify the best cutoff for this purpose in a sample of cancer pain individuals. The secondary aim was to compare ESAS-D with another commonly used screening measure (Patient Health Questionnaire-2 [PHQ-2]) for classifying individuals as meeting or not meeting Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for depression. METHODS: 49 cancer pain individuals completed the ESAS-D and PHQ-2 Within 2 weeks, a psychiatrist interviewed the participants and determined whether or not they met criteria for MDD based on the DSM-5. RESULTS: The ESAS-D demonstrated acceptable accuracy and validity for classifying MDD. A cutoff of ≥2 was identified as being best able to balance sensitivity (85%) and specificity (76%) and had an overall accuracy of 79%. A receiver operating characteristic curve analysis showed an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.68-0.94). The ESAS-D also compared favorably with the modified Thai PHQ-2 (sensitivity, 75%; specificity, 72%; overall accuracy, 73%; AUC, 0.74 [95% CI: 0.59-0.88]) for identifying MDD individuals. CONCLUSIONS: The ESAS-D showed acceptable sensitivity, specificity, and overall accuracy for screening for MDD in cancer and pain. It could therefore be used to screen for probable depression in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".