Reliability and validity of the Japanese version of Pain Disability Index
Bibliographic record
Abstract
This study evaluated the reliability and validity of a Japanese version of Pain Disability Index (PDI). Analyses were conducted on a 7-item version (PDI-J) and a 5-item (PDI-5-J version of the PDI). Using a web-based survey system, we recruited 300 individuals with chronic low back pain (lasting ≥3 months) and 300 individuals with chronic daily headache (lasting ≥15 days per month for 3 months) aged 20-64 years. Analyses revealed a one-factor with goodness-of-fit indices assessed by confirmatory factor analysis. For concurrent validity, we calculated Pearson's correlation coefficients among the PDI-J, PDI-5-J, Pain Disability Assessment Scale, Pain numerical rating scale, and revised version of Short-Form McGill Pain Questionnaire. Internal consistency was evaluated by Cronbach's α, and test-retest reliability was assessed with intraclass correlations (ICCs) in 100 of 600 participants a week after the first response. Both Japanese adaptations of the PDI demonstrated good concurrent validity and reliability (Cronbach's α was 0.89 for PDI-J in chronic low back pain or chronic daily headache, and 0.94 and 0.93 for PDI-5-J in chronic low back pain and chronic daily headache, respectively). The PDI-J and PDI-5-J showed were highly correlated (r = 0.98). ICCs were 0.67 and 0.59 for the PDI-J and 0.59 and 0.63 for the PDI-5-J in chronic low back pain and chronic daily headache, respectively. In conclusion, these two PDI versions can be potentially used for evaluating pain-related interference with daily activities among the Japanese general population.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".