The impact of non–injury-related factors on disability secondary to whiplash associated disorder type II: a retrospective file review
Bibliographic record
Abstract
BACKGROUND: There is evidence to suggest that Whiplash Associated Disorders (WADs) are influenced by physical trauma and psychosocial factors, as well as by medicolegal and compensation systems. OBJECTIVE: To investigate the impact of noninjury related variables on self-reported disability at initial assessment among patients presenting with WAD type II injuries. DESIGN AND SETTING: We reviewed a total of 1101 consecutive files of patients presenting to a single chiropractor's office in British Columbia, Canada. We included those who met the inclusion criteria. We extracted demographic variables and noninjury related information from 33 eligible patient files. We calculated correlations between variables and created a multivariable linear regression model to evaluate their relative associations with Neck Disability Index (NDI) scores on presentation. RESULTS: Higher NDI scores on initial assessment correlated with female sex (r = 0.40, P =.02), a greater number of subsequent treatments (r = 0.44, P =.01), a higher number of providers seen before presentation (r = 0.40, P =.02), and most strongly with the involvement of a lawyer (r = 0.73, P <.01). A multivariable linear regression model found that only female sex (P =.03) and the involvement of a lawyer (P =.01) remained significantly associated with higher NDI scores on presentation (adjusted R2 = 0.68 for the model). Female sex was associated with a 10-point increase in NDI scores on presentation (beta coefficient = 10.5; 95% confidence interval [CI] 2.8-18.2), and involvement of a lawyer was associated with a 15-point increase in NDI scores on presentation (beta coefficient = 14.9; 95% CI 5.0-24.7). CONCLUSION: Our analysis of WAD type II patients in receipt of compensation found that higher self-reported disability on initial assessment was associated with female sex and in particular by retaining a lawyer. Large prospective studies are needed to establish the validity of these findings.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".