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Record W2019374579 · doi:10.1093/eurheartj/ehl147

Vasovagal syncope in medical students and their first-degree relatives

2006· article· en· W2019374579 on OpenAlexaffabout
Anna Serletis, Sarah Rose, Aaron Sheldon, Robert S. Sheldon

Bibliographic record

VenueEuropean Heart Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineVasovagal syncopeSyncope (phonology)First-degree relativesDegree (music)CardiologyInternal medicineFamily medicineFamily history

Abstract

fetched live from OpenAlex

AIMS: To determine the effect of family history on the likelihood of vasovagal syncope. METHODS AND RESULTS: Sixty-two medical students and 228 first-degree relatives were studied. Vasovagal syncope was ascertained with the Calgary syncope symptom score. The effects of the sex of the subject and parental syncope history on the likelihood of offspring fainting were described using Kaplan-Meier estimates and analysed using proportional hazards regression. The prevalence of vasovagal syncope was 32% and the median age of first faint in those who fainted was 14 years. More females than males fainted [42 vs. 31%; P=0.02; hazard ratio (HR) 1.34 (95% CI 1.07-1.68)]. An individual with two fainting parents was more likely to faint than one with no fainting parents [P<0.0001; HR 3.4 (95% CI 1.7-7.03)]. In the proportional hazards model, offspring of either sex whose mother faints are more likely to faint than those whose mother does not faint [HR 2.86 (95% CI 1.54-5.31)]. Having a father who faints significantly increases the risk of syncope in sons [HR 4.12 (95%CI 1.39-12.31)], but not in daughters [HR 1.18 (95% CI 0.56-3.34)]. CONCLUSION: Family history and sex of subject are important predictors of vasovagal syncope in offspring.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.028
GPT teacher head0.286
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations228
Published2006
Admission routes2
Has abstractyes

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