Assessment of emotional distress in palliative care: Edmonton Symptom Assessment System-revised (ESAS-r) vs Distress Thermometer
Bibliographic record
Abstract
OBJECTIVES: To evaluate the sensitivity and specificity of the Distress Thermometer (DT) as a screening tool for emotional distress in oncological palliative care patients and to compare the DT with the Edmonton Symptom Assessment System-revised (ESAS-r) and the gold standard to determine the most appropriate assessment method in palliative psychological care. METHODS: Data were collected from psychological screening tests (ESAS-r and DT), and clinical interviews (gold standard) were conducted by a clinical psychologist specialist in palliative oncology from January 2021 to January 2022 in an oncology palliative care service. RESULTS: = 206; 57.9%), 60.4% were married/with a partner, 55.4% had between 6 and 9 years of schooling, and a median age of 57 (range, 46-65) years. The cutoff of the DT was 5, with a sensitivity of 75.88% and specificity of 54.3%. Emotional problems (sadness and nervousness) had a greater area under the curve (AUC) when measured using the DT than the ESAS-r; however, only in the case of the comparative sadness and discouragement was the difference between the AUC marginally significant. SIGNIFICANCE OF RESULTS: The use of the DT as a screening tool in oncological palliative care is more effective in the evaluation of psychological needs than the ESAS-r. The DT, in addition to evaluation by an expert psychologist, allows for a more comprehensive identification of signs and symptoms to yield an accurate mental health diagnosis based on the International Classification of Diseases-11th Revision and/or Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".