Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional study. OBJECTIVE: To analyze the psychometric properties of the neck disability index (NDI), with a special emphasis in its factor structure, and its usefulness, in a sample of patients suffering from a subacute whiplash problem. SUMMARY OF BACKGROUND DATA: A valid and reliable instrument to assess pain-related disability would be of great help to clinicians and researchers interested in whiplash associated disorders. First, to better understand the impact of whiplash on the patient's life, and his or her progress over time. Second, to formulate comprehensive treatment plans, and evaluate the results from therapeutic actions. Finally, to follow-up patients' changes and improvement. The NDI could be an appropriate instrument for these purposes. METHODS: A convenience sample of 150 subacute whiplash patients participated. They were requested to complete the Catalan version of the NDI, and report about their pain intensity, pain interference and depression. RESULTS.: An exploratory factor analysis showed that the NDI can be viewed as a 2-factor instrument. The items and the instrument's total score were normally distributed. Internal consistency was also appropriate both for the total score (Cronbach's alpha: 0.87) and the 2 subscales (0.7 for the pain and interference with cognitive functioning scale, and 0.83 for the physical functioning scale). Total NDI and subscales scores significantly correlated with pain intensity, pain interference, and depression. CONCLUSION: The NDI showed excellent psychometric properties in a sample of subacute whiplash patients. Additional research is needed to replicate the NDIs factor structure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".