Single Questions for the Screening of Anxiety and Depression in Hemodialysis
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are common and underrecognized in end-stage renal disease (ESRD), are associated with poor outcomes and reduced health-related quality of life, and are potentially treatable. Simple, accurate screening tools are needed. OBJECTIVE: We examined the operating characteristics of single questions for anxiety and depression from the Edmonton Symptom Assessment System (ESAS) in hemodialysis. DESIGN: Cross-sectional study. SETTING: Two outpatient hemodialysis units (1 tertiary, 1 community) in Hamilton, Canada. PATIENTS: Adult prevalent hemodialysis patients. MEASUREMENTS: ESAS and Hospital Anxiety and Depression Scale (HADS). METHODS: Participants were asked the degree to which they experienced anxiety and depression using the ESAS. ESAS single questions for anxiety and depression were compared with the reference standard of the HADS using dialysis population specific cutoffs (HADS anxiety subscale ≥6 and HADS depression subscale ≥7). Logistic regression was used to create receiver operating characteristics (ROC) curves. RESULTS: We recruited 50 participants with a mean age of 64 (SD = 12.4) years, of whom 52% were male and 96% were on ≥3× weekly hemodialysis. Using the reference standards, 28 (56%) had a diagnosis of anxiety and 27 (54%) had a diagnosis of depression. Areas under the ROC curves were 0.83 for anxiety and 0.81 for depression using ESAS scores of ≥2. LIMITATIONS: Sample size and the lack of a reference gold standard. CONCLUSIONS: The ESAS single questions for anxiety and depression have reasonable discrimination in a hemodialysis population. The use of more complex and time-consuming screening instruments could be reduced by adopting the ESAS questions for anxiety and depression in hemodialysis.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".