A Population-Based Case-Series of Ontario Patients Who Develop a Vertebrobasilar Artery Stroke After Seeing a Chiropractor
Bibliographic record
Abstract
PURPOSE: The current evidence suggests that association between chiropractic care and vertebrobasilar artery (VBA) stroke is not causal. Rather, recent epidemiological studies suggest that it is coincidental and reflects the natural history of the disorder. Because neck pain and headaches are symptoms that commonly precede the onset of a VBA stroke, these patients might seek chiropractic care while their stroke is in evolution. However, very little is known about the characteristics of these patients. In fact, only small clinical case series and physician surveys have described the characteristics of chiropractic patients who later develop a VBA stroke. To date, no population-based study has described this group of patients. Therefore, the objective of our study is to describe the characteristics of Ontario VBA stroke patients who consulted a chiropractor within the year before their stroke. METHODS: We conducted a population-based case series using administrative health care records of all Ontario residents hospitalized with VBA stroke between April 1, 1993, and March 31, 2002. Three databases were deterministically linked to extract the relevant information. We describe the demographic, health care utilization, and comorbidities of VBA patients. RESULTS: Ninety-three VBA stroke cases consulted a chiropractor during the year before their stroke. The mean age was 57.6 years (SD, 16.1), and 50% were female. Most cases had consulted a medical doctor during the year before their stroke, and 75.3% of patients had at least one cerebrovascular comorbidity. The 3 most common comorbidities were neck pain and headache (prevalence, 66.7%; 95% confidence interval [CI], 57.0%-76.3%), diseases of the circulatory system (prevalence, 63.4%; 95% CI, 54.8%-74.2%), and diseases of the nervous system and sense organs (prevalence, 47.3%; 95% CI, 38.7%-58.1%). CONCLUSIONS: Our population-based analysis suggests that VBA stroke patients who consulted a chiropractor the year before their stroke are older than previously documented in clinical case series. We did not find that women were more commonly affected than men. Moreover, we found that most patients had at least one cardio- or cerebrovascular comorbidity. Our analysis suggests that relying on case series or surveys of health care professionals may provide a biased view of who develops a VBA stroke.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".