Structured Interview Assessment of Symptoms and Concerns in Palliative Care
Bibliographic record
Abstract
OBJECTIVE: Assessment in palliative care requires a multidimensional review of physical symptoms and psychosocial concerns in a format appropriate for patients with advanced illness. In this study, we describe the initial development and validation of a structured interview for assessing common symptoms and concerns faced by terminally ill individuals. METHOD: We constructed a 13-item Structured Interview for Symptoms and Concerns (SISC) based on a review of end-of-life issues and administered it to 69 patients receiving palliative care for advanced cancer. Along with the interview, each participant completed visual analog scales (VAS) addressing the same constructs. Test-retest and interrater reliability were determined, as was the concordance between interview ratings and VAS scores. RESULTS: Overall, the interview items had excellent interrater reliability (intraclass correlations were > 0.90) and at least moderate temporal stability (test-retest correlations ranged from 0.50 to 0.90). Concurrent validity was evident in the good concordance between interview items and VAS measures (correlations were > 0.70). The SISC was also sensitive to individual differences between subgroups of participants who did or did not meet diagnostic criteria for anxiety or depressive disorders. CONCLUSIONS: This study demonstrates that structured interviews provide a reliable and valid approach to assessment in palliative care and may be an appropriate alternative for some research applications.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".