The Accuracy of the Edmonton Symptom Assessment System for the Assessment of Depression in Patients With Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Depression is a common health problem among patients with cancer. The Edmonton Symptom Assessment System (ESAS) is one of many tools that have been used to evaluate depression in these patients. Nevertheless, the diagnostic performance and the appropriate cutoff point of the ESAS for the assessment of depression in these patients have varied in the studies. Purpose: To determine the diagnostic accuracy and the optimal cutoff point for the ESAS for the assessment of depression in patients with cancer. Data Sources: PubMed, Scopus, CINAHL, and Cochrane library databases from inception to September 18, 2016. Study Selection: Paired reviewers independently screened abstracts and full-text articles for all cross-sectional studies published in English and compared these with the ESAS in the depression (ESAS-D) subscale with reference to standard tests for the assessment of depression. Data Extraction: Two reviewers serially abstracted the data and independently assessed the risk of bias by using the Quality Assessment of Diagnostic Accuracy Studies 2. Data Synthesis: A total of 6 studies were eligible for review. Our meta-analysis showed the optimal cutoff point of the ESAS-D ≥ 4, with pooled sensitivity and specificity at 53% (95% confidence interval [CI]: 38%-67%) and 90% (95% CI: 82%-94%), respectively. The positive likelihood ratio and diagnostic odds ratio of the ESAS-D ≥ 4 were 5.2 (95% CI: 3.1-8.6) and 10 (95% CI: 5-19). There was a high degree of heterogeneity between the studies ( P value <.001, I 2 = 96%). Conclusion: We suggest that an ESAS-D ≥ 4 could be used to detect possible cases of depression in patients with cancer. Registration: Our study protocol was registered with the International Prospective Register of Systematic Reviews on October 4, 2016, and was last updated on January 11, 2017 (registration number CRD42016048288).
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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 | 0.030 | 0.082 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.047 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".