Development and preliminary validation of the pancreatic cancer disease impact score
Bibliographic record
Abstract
OBJECTIVE: Patient-reported outcomes are important for clinical practice and research, and should reflect what patients perceive as important. The objective of this study was to develop and preliminarily validate a brief, patient-derived, disease-specific tool, the pancreatic cancer disease impact (PACADI) score. METHODS: The development was performed in two phases. Forty-one patients with confirmed pancreatic cancer (PC) selected dimensions of health related to the impact of the disease. A weighting of the eight most frequently reported dimensions was performed in a second sample of 80 PC patients who also rated the impact on eight numeric rating scales (NRS, range 0 to 10). The relative weights and the scores from the NRS were used to compute the PACADI score (range 0 to 10). The patients also completed Edmonton Symptom Assessment System (ESAS) and EQ-5D. RESULTS: Dimensions reported by more than 20% of the patients were included in the PACADI score (relative weights in parenthesis): pain/discomfort (0.16), fatigue (0.16), anxiety (0.15), bowel/digestive problems (0.14), loss of appetite (0.13), dry mouth (0.11), itchiness (0.08), and nausea (0.07). The PACADI score in the 80 PC patients had a mean (SD) value of 3.26 (2.06) (95% CI 2.80, 3.71), was moderately to strongly correlated to ESAS sense of well-being (r = 0.69) and EQ-5D (r = -0.52), and discriminated significantly between patients with and without PC. CONCLUSION: The PACADI score is a new eight-item, patient-derived, disease-specific measure. Preliminary validation regarding construct validity and discrimination encourages further validation in independent patient samples.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".