Frequency of injuries associated with syncope in the prevention of syncope trials
Bibliographic record
Abstract
AIMS: Syncope can lead to injuries. We determined the frequency, severity, and predictors of injuries due to syncope in cohorts of syncope patients. METHODS AND RESULTS: Participants were enrolled in the POST2 (fludrocortisone) and POST4 (midodrine) vasovagal syncope (VVS) randomized trials, and POST3 enrolled patients with bifascicular block and syncope. Injury was defined as minor (bruising, abrasions), moderate (lacerations), and severe (fractures, burns, joint pain), and recorded up to 1 year after enrolment. A total of 459 patients (median 39 years) were analysed. There were 710 faints occurred in 186 patients during a 1-year follow-up. Fully 56/186 (30%) of patients were injured with syncope (12% of overall group). There were 102 injuries associated with the 710 faints (14%), of which 19% were moderate or severe injuries. Neither patient age, sex, nor the presence of prodromal symptoms associated with injury-free survival. Patients with bifascicular block were more prone to injury (relative risk 1.98, P = 0.018). Patients with ≥4 faints in the prior year had more injuries than those with fewer faints (relative risk 2.97, P < 0.0001), but this was due to more frequent syncope, and not more injuries per faint. In VVS patients, pharmacological therapy significantly reduced the likelihood of an injury due to a syncopal spell (relative risk 0.64, P = 0.015). Injury severity did not associate with age, sex, or prior-year syncope frequency. CONCLUSION: Injuries are frequent in syncope patients, but only 4% of injuries were severe. None of age, sex, and prodromal symptoms associate with injury.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".