Psychometric performance of the PAncreatic CAncer disease impact (PACADI) score
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Pancreatic Cancer Disease Impact (PACADI) score measures the impact of pancreatic cancer (PC) on important health dimensions, selected by patients. The aim of this single center study was to test the psychometric performance of the Pancreatic Cancer Disease Impact (PACADI) score. METHODS: Patients with suspected pancreatic cancer (PC) completed PACADI, the EuroQol-5D (EQ-5D index) and Edmonton Symptom Assessment System (ESAS) in this longitudinal observational study. Measures were compared across patients with PC (n = 210), other malignant lesions (OML) (n = 109) and non-malignant lesions (NML) (n = 41). Associations, test-retest and internal consistency reliability, longitudinal changes, sensitivity to change and prediction of mortality during the first year were examined in patients with PC. RESULTS: The three measures discriminated between PC and OML. The PACADI score correlated strongly at baseline (n = 199)/after three months (n = 85) with the EQ-5D index and ESAS "sense of well-being" (0.64 and 0.66/0.73 and 0.69, p < 0.001, respectively), showed high test-retest reliability (ICC 0.84) and very good internal consistency reliability (Cronbach's alpha 0.81-0.85) across all visits. Scores improved over time at 3, 6, 9 and 12 months for survivors, and standardized response mean (SRM) for improvement between 2 and 3 months (n = 44) was 0.80 (PACADI), -0.59 (EQ-5D index) and 0.69 (ESAS "sense of well-being"). The PACADI score significantly predicted mortality within the first year (p = 0.02) in contrast to the EQ-5D index and ESAS "sense of well-being". CONCLUSION: This study showed satisfactory psychometric performance of the PACADI score. The results support its use in clinical practice and intervention trials.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".