Diagnosing vasovagal syncope based on quantitative history-taking: validation of the Calgary Syncope Symptom Score
Bibliographic record
Abstract
AIMS: It can be difficult to distinguish vasovagal syncope, the most common cause of transient loss of consciousness (T-LOC), from other causes of syncope by history taking. The Calgary Syncope Symptom Score (Calgary Score) is a tool developed for this purpose. We studied its performance in a series of patients presenting with T-LOC. METHODS AND RESULTS: We calculated the Calgary Score for 380 patients presenting with T-LOC to a number of departments of our university hospital. Diagnoses of vasovagal syncope based on the Calgary Score were then compared with the final diagnosis, obtained after additional testing and 2 years of follow-up. The sensitivity of the Calgary Score was 87% (95% CI: 82-91%), at a specificity of 32% (95% CI: 24-40%). Most items of the Calgary Score were less discriminative in our study group than in the original population. Incorrectly labelling patients with syncope as vasovagal was most common in patients with psychogenic pseudosyncope (specificity 21%) but also occurred in patients with cardiac syncope (specificity 32%). CONCLUSION: The sensitivity of the Calgary Score was comparable with the one in the original study, but its specificity was much lower, limiting its value in patients presenting with T-LOC in a general hospital setting.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".