Incidence and predictors of chronic headache attributed to whiplash injury
Bibliographic record
Abstract
We identified clinical, demographic and psychological predictive factors that may contribute to the development of chronic headache associated with mild to moderate whiplash injury [Quebec Task Force (QTF) ≤ II] and determined the incidence of this chronic pain state. Patients were recruited prospectively from six participating accident and emergency departments. While 4.6% of patients developed chronic headache attributed to whiplash injury according to the International Classification of Headache Disorders, 2nd edn criteria, 15.2% of patients complained about headache lasting > 42 days (QTF criteria). Predictive factors were pre-existing facial pain [odds ratio (OR) 9.7, 95% confidence interval (CI) 2.1, 10.4; P = 0.017], lack of confidence to recover completely (OR 5.5, 95% CI 2.0, 13.2; P = 0.005), sore throat (OR 5.0, 95% CI 1.5, 8.9; P = 0.013), medication overuse (OR 4.2, 95% CI 1.4, 12.3; P = 0.009), high Neck Disability Index (OR 4.0, 95% CI 1.3, 12.6; P = 0.019), hopelessness/anxiety (OR 3.8, 95% CI 1.3, 8.7; P = 0.024), and depression (OR 3.3, 95% CI 1.2, 9.4; P = 0.024). The lack of a control group limits the conclusions that can be drawn from this study. Identified predictors closely resemble those found in chronic primary headache disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".