Likelihood of injury due to vasovagal syncope: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: Vasovagal syncope (VVS) is the most common type of syncope and is usually considered a benign disorder. The potential for injury is worrisome but the likelihood is unknown. We aimed to determine the proportion of patients injured due to VVS. METHODS AND RESULTS: A systematic search of studies published until August 2020 was performed in multiple medical and nursing databases. Included studies had data on the proportion of patients with injury due to VVS prior to study enrolment. Random effects methods were used. Twenty-three studies having 3593 patients met inclusion criteria. Patients were diagnosed clinically with VVS, and 82% had >2 syncopal episodes before enrolment. Tilt test was positive in 60% and 14 studies reported comorbidities (32.6% hypertensive). The weighted mean injury rate was 33.5% [95% confidence interval (CI): 27.3-40.5%]. The likelihood of injury correlated with population age (r = 0.4, P = 0.05), but not with sex, positive tilt test, or hypertension. The injury rates were 25.7% (95% CI: 19.1-32.8%) in studies with younger patients (mean age ≤50 years, n = 1803) and 43.4% (95% CI: 34.9-52.3%) in studies with older patients (P = 0.002). Nine studies reported major injuries; with a weighted mean rate of major injuries of 13.9% (95% CI: 9.5-19.8%). CONCLUSION: Injuries due to syncope are frequent, occurring in 33% of patients with VVS. The risk of major injuries is substantial. Older patients are at higher risk. Clinicians should be aware of the risk of injuries when providing care and advice to patients with VVS.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".