Characterizing the Physical and Psychological Experiences of Newly Diagnosed Pancreatic Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Pancreatic cancer is a devastating disease with a poor prognosis, causing significant physical and psychological distress that detrimentally impacts patients' quality of life. AIM: This study aimed to comprehensively assess the physical and psychological status of newly diagnosed pancreatic cancer patients. METHODS: A cohort of 138 newly diagnosed patients completed standardized assessments, including the Edmonton Symptom Assessment System (ESAS), Patient Health Questionnaire-9 (PHQ-9), Mini-Mental State Examination (MMSE), and Distress Thermometer (DT). Data were analysed using descriptive statistics. RESULTS: The ESAS scores revealed high symptom burden, with mean scores of 6.8 for pain, 7.2 for fatigue, and 4.9 for depression. Measures of well-being indicated low scores, with means of 2.3 for physical well-being, 1.5 for social/family well-being, and 1.7 for emotional well-being. Distress levels were also high, with a mean score of 7.6 on the DT. CONCLUSION: Newly diagnosed pancreatic cancer patients experience substantial physical and psychological challenges, including severe symptom burden, distress, depressive symptoms, and cognitive impairment. Holistic care approaches that prioritize symptom management and address psychological distress are essential to improve patient outcomes and enhance overall well-being.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".