Translation and validation of the Pancreatic Cancer Disease Impact score for Chinese patients with pancreatic cancer: A methodological and cross-sectional study
Bibliographic record
Abstract
Objective We aimed to establish and validate the Chinese version of the Pancreatic Cancer Disease Impact (C-PACADI) score for Chinese patients with pancreatic cancer (PC). Methods This was a methodological and cross-sectional study. We established the C-PACADI score following Beaton's translation guidelines and then included 209 patients with PC to evaluate C-PACADI's reliability and validity. Results The Cronbach's alpha coefficient of the C-PACADI score was 0.822. The correlation coefficient between "skin itchiness" score and the total score was 0.224, while the correlation coefficients ranged from 0.515 to 0.688 ( P < 0.001) for all the other items. The item content validity index and the scale content validity index, evaluated by eight experts were 0.875 and 0.98, respectively. Regarding concurrent validity, the total score of the C-PACADI score was moderately correlated with the EuroQol-5D (EQ-5D) index and the EQ-5D VAS score ( r = −0.738, P < 0.01; r = −0.667, P < 0.01, respectively); the individual-item scores of C-PACADI on pain/discomfort, anxiety, loss of appetite, fatigue, and nausea were strongly associated with the corresponding symptoms of the Edmonton Symptom Assessment System scale ( r ranged from 0.879 to 0.916, P < 0.01). The known-group validity was demonstrated by C-PACADI's ability to detect significant symptom differences between groups stratified by treatment modalities ( P < 0.05) and health status ( P < 0.001). Conclusions The C-PACADI score is a suitable disease-specific tool for measuring the prevalence and severity of multiple symptoms in the Chinese population with PC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".