Frequency of anxiety and depression and screening performance of the Edmonton Symptom Assessment Scale in a psycho‐oncology clinic
Bibliographic record
Abstract
OBJECTIVE: The primary objective of this study was to determine the frequency of screening instrument-detected depression and anxiety in outpatients on initial presentation to a consultation psychiatric oncology clinic. The secondary objectives were to identify characteristics associated with depression and anxiety among these patients, and to determine the optimal cut-off score for the ESAS-Anxiety (ESAS-A) and ESAS-Depression (ESAS-D) items, using the Patient Health Questionnaire (PHQ-9) and the General Anxiety Disorder Scale (GAD-7) as a gold standard in cancer patients. METHODS: A retrospective chart review was conducted for 1221 consecutive cancer patients seen in the Psychiatric Oncology Center as an initial consult between June 1, 2014 and January 31, 2017. RESULTS: When the cutoff was 10 for the PHQ-9 and the GAD-7, 60% of patients self-reported depression and 51% self-reported anxiety. When the cutoff was 15 (severe symptom) for the PHQ-9 and GAD-7, approximately 30% and 27% of the patients had severe depression or anxiety, respectively. Age and gender were found to be associated with anxiety. An ESAS cutoff value of ≥3 for depression and ≥5 for anxiety resulted in sensitivity of 0.84 and 0.85 when using PHQ 9 ≥ 10 for depression and GAD 7 ≥ 10 for anxiety, respectively. CONCLUSIONS: Self-reported depression and anxiety are frequent symptoms among patients at a psychiatric oncology center for an initial visit. ESAS-A and ESAS-D have good sensitivity for anxiety and depression screening of cancer patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".