Mid-term follow-up of whiplash with Bournemouth Questionnaire: The significance of the initial depression to pain ratio
Bibliographic record
Abstract
INTRODUCTION: The Bournemouth Questionnaire (BQ) was used to report the short to mid-term outcome of a prospective cohort of patients who had sustained Whiplash Associated Disorder (WAD), and establish whether outcome could be predicted on initial assessment. METHODS: One hundred patients with WAD grades I-III on the Quebec Task Force Classification were referred for physiotherapy (neck posture advice, initially practised under the direct supervision of a therapist). BQ scores were recorded on the first visit, at six weeks, then at final follow-up. RESULTS: Seventy-six percent of patients were available at final follow-up, 58% women. The mean age was 43.2 years old and follow-up time 38 months (28-48). Symptoms plateaued after six weeks in the majority and improved gradually thereafter. When the individual BQ components on initial presentation were reassessed, patients who score disproportionately highly in BQ Question 5 (Depression) had a worse outcome. To quantify this, the ratio of BQ Questions 5 (Depression)/1 (Pain) was calculated. BQ5/1 ratio greater than 1 on initial presentation had an odds ratio of 2 for poor outcome (p= 0.02). CONCLUSION: The BQ can therefore be used to identify patients with a disproportionately high depression score (BQ5) who are highly likely to clinically deteriorate in the medium term.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".