Are Prevalent Self-reported Cardiovascular Disorders Associated With Delayed Recovery From Whiplash-associated Disorders
Bibliographic record
Abstract
OBJECTIVES: The aim of this cohort study was to investigate the association between self-reported cardiovascular disorders (CVD) and recovery from whiplash-associated disorder (WAD) after a traffic collision. MATERIALS AND METHODS: This study was based on the Saskatchewan Government Insurance cohort, including individuals over 18 years of age, who made a traffic-injury claim or received health care after a traffic injury, between 1997 and 1999. Participants completed a baseline questionnaire and were followed up by telephone interviews at 6 weeks, 3 months, 6 months, 9 months, and 12 months after injury. Our sample includes a subcohort of 6011 participants who reported WAD (defined as answering "yes" to the question "Did the accident cause neck or shoulder pain") at baseline. The outcome, self-perceived recovery, was measured at all follow-up interviews. The presence of CVD and its effect on health was classified into 3 exposure categories: (1) CVD absent, (2) CVD present with no or mild effect on health, and (3) CVD present with moderate or severe effect on health. The association between CVD and recovery from WAD was assessed with Cox regression, and adjusted for potential confounders. RESULTS: We found a crude association between comorbid CVD with moderate or severe effect on health in women. However, the adjusted association was weak and potentially affected by residual confounding. We found no association in men. DISCUSSION: Our results suggest that CVD does not have an impact on the recovery of individuals with WAD.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".