Associations between the injustice experience questionnaire and treatment term in patients with acute Whiplash-associated disorder in Japan: Comparison with Canadian data
Bibliographic record
Abstract
INTRODUCTION: This study aimed to investigate the differences in the Injustice Experience Questionnaire (IEQ) scores during the early period after the diagnosis of Whiplash-associated disorder (WAD) between Japanese and Canadian samples, and the associations between the IEQ scores and treatment terms in Japanese patients with acute WAD. METHODS: We used secondary data for the IEQ scores of Canadian patients with acute WAD. In Japan, we collected data from 85 consecutively enrolled patients with acute WAD, and their treatment terms were collected; these referred to the number of days between the date of injury and the closure date of the insurance claim and the number of treatment visits. Before treatment, the Numeric Rating Scale, Neck Disability Index, Hospital Anxiety and Depression Scale, IEQ, and Euro Quality of Life five-dimensional questionnaire were administered. The variables were subjected to multivariate analysis with each treatment term. RESULTS: The IEQ scores were higher in Japan than in Canada. Through multiple regression analysis, IEQ scores were independently correlated with treatment terms. The optimal cutoff point of the IEQ scores for a prolonged treatment term was 21 and 22 points, respectively. CONCLUSIONS: The IEQ scores were associated with treatment terms in patients with acute WAD in Japan.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".