Neuroticism as a moderator of symptom-related distress and depression in 4 noncancer end-of-life populations
Bibliographic record
Abstract
OBJECTIVES: Neuroticism is a significant predictor of adverse psychological outcomes in patients with cancer. Less is known about how this relationship manifests in those with noncancer illness at the end-of-life (EOL). The objective of this study was to examine the impact of neuroticism as a moderator of physical symptoms and development of depression in patients with amyotrophic lateral sclerosis (ALS), chronic obstructive pulmonary disease (COPD), end-stage renal disease (ESRD), and frailty in the last 6 months of life. METHODS: = 102) in the estimated last 6 months of life, with a range of illness-related symptoms, assessed longitudinally at 2 time points. We examined neuroticism as a moderator of illness-related symptoms at Time 1 (∼6 months before death) and depression at Time 2 (∼3 months before death) using ordinary least squares regression. RESULTS: Results revealed that neuroticism significantly moderated the relationship between the following symptoms and depression measured 3 months later: drowsiness, fatigue, shortness of breath, wellbeing (ALS); drowsiness, trouble sleeping, will to live, activity (COPD); constipation (ESRD); and weakness and will to live (frailty). SIGNIFICANCE OF RESULTS: These findings suggest that neuroticism represents a vulnerability factor that either attenuates or amplifies the relationship of specific illness and depressive symptoms in these noncancer illness groups at the EOL. Identifying those high in neuroticism may provide insight into patient populations that require special care at the EOL.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".