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
ObjectiveWe aimed to establish and validate the Chinese version of the Pancreatic Cancer Disease Impact (C-PACADI) score for Chinese patients with pancreatic cancer (PC).MethodsThis 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.ResultsThe 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).ConclusionsThe 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".