MétaCan
Menu
Back to cohort
Record W2915013967 · doi:10.3390/jcm8020150

Syncope in a Working-Age Population: Recurrence Risk and Related Risk Factors

2019· article· en· W2915013967 on OpenAlexaff
Franca Barbic, Franca Dipaola, Giovanni Casazza, Marta Borella, Maura Minonzio, Monica Solbiati, Satish R. Raj, Robert S. Sheldon, James Quinn, Giorgio Costantino, Raffaello Furlan

Bibliographic record

VenueJournal of Clinical Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineSyncope (phonology)Risk factorPopulationCardiologyInternal medicinePediatricsEnvironmental health

Abstract

fetched live from OpenAlex

Syncope in a worker undertaking risky tasks may result in fatalities for the individual or for third parties. We aimed at assessing the rate of syncope recurrence and the risk factors underlying the likelihood of syncope relapse in a working-age population. A prospective cohort of all patients aged 18⁻65 years consecutively admitted to the Emergency Department for syncope was enrolled. Risk of syncope relapse was assessed at a six-month, 1-year, and 5-year follow-up. Predictors of syncope recurrence have been evaluated at six months and 1 year from the syncope index by a multivariable logistic regression analysis. 348 patients were enrolled. Risk of syncope relapse was 9.2% at 6 months, 11.8% at 1 year, and 23.4% at 5 years. At 6-month follow-up, predictor of syncope recurrence was ≥3 prior lifetime syncope episodes. At 1-year, ≥3 prior lifetime syncope episodes, diabetes mellitus, and anaemia were risk factors for syncope relapse. There was an exceeding risk of recurrence in the first 6 months and a reduced risk of 3.5% per year after the first year. Anaemia, diabetes mellitus, and prior lifetime syncope burden are of importance when giving advice about the resumption of "high risk" jobs following a syncope episode.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.115
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.353
Teacher spread0.317 · 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 teacher head, 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

Citations16
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical MedicineSame topicCardiovascular Syncope and Autonomic DisordersFrench-language works237,207