A screening method for sleep disturbances at the end-of-life
Bibliographic record
Abstract
OBJECTIVE: To evaluate sleep disturbances and to verify the accuracy of three screening tests to detect them in patients at the end-of-life admitted in a hospital palliative care unit. METHOD: The level of sleep disturbances was evaluated through the Pittsburgh Sleep Quality Index (PSQI) in 150 palliative patients. This questionnaire was the criterion variable for testing the three screening tests used: Edmonton Symptom Assessment System (ESAS-Sleep subscale); the single question "How much do you worry about your sleep problems?" which is answered on a scale of 0-10 (Sleep-Worry-Q) and another single question: "Do you think you have sleep problems?" with two response categories, Yes/No (Sleep-Problem-Q). RESULTS: According to the PSQI (cut-off point: 8), 87% of patients presented sleep disturbances. The ESAS-Sleep (cut-off point: 3) showed a sensitivity of 0.87, a specificity of 0.58, and an AUC of 0.729; the Sleep-Worry-Q (cut-off point: 4) showed a sensitivity of 0.95, a specificity of 0.68, and an AUC of 0.854; the Sleep-Problem-Q obtained a sensitivity of 0.92 and a specificity of 0.65. SIGNIFICANCE OF RESULTS: Patients at the end-of-life, near the time of death, have high levels of sleep disturbances that can be detected early, with better diagnostic accuracy, with the Sleep-Worry-Q. Although from a clinical point of view, the application of the Sleep-Problem-Q may be more advantageous, as it presents good diagnostic accuracy, greater simplicity, and brevity.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".