Assessing Preparatory Grief in Advanced Cancer Patients as an Independent Predictor of Distress in an American Population
Bibliographic record
Abstract
BACKGROUND: Grief is a universal experience for patients living with a terminal illness, but it is not routinely measured. The Preparatory Grief in Advanced Cancer (PGAC) instrument has been used in Greece, but this is its first use in an American population with advanced cancer. OBJECTIVE: Our aim was to use the PGAC instrument in an American population of advanced cancer patients to explore demographic, clinical, and psychological factors that may predict higher preparatory grief. DESIGN: Subjects completed a single cross-sectional time point evaluation. SETTING/SUBJECTS: Fifty-three adult outpatients and inpatients with incurable solid malignancies from Chicago, IL. MEASUREMENTS: Demographic and clinical information, the PGAC instrument, the Hospital Anxiety and Depression Scale (HADS), the distress thermometer (DT), the Edmonton Symptom Assessment Scale (ESAS), and a quality-of-life (QOL) 2-question scale. RESULTS: The mean PGAC score was 26.9 (range 0-70) and was only correlated with DT in multivariate analysis. CONCLUSIONS: Preparatory grief was a common experience, and one-fourth of our sample participants had significant grief. Distress was the only independent factor (including psychological, physical, clinical, or demographic factors) correlated with higher preparatory grief scores.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".