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Record W2954244608 · doi:10.1001/jama.2019.8001

Did This Patient Have Cardiac Syncope?

2019· review· en· W2954244608 on OpenAlexaff
Omar T. Albassam, Robert J. Redelmeier, Steven Shadowitz, Aatif M. Husain, David L. Simel, Edward Etchells

Bibliographic record

VenueJAMA · 2019
Typereview
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineSyncope (phonology)Atrial fibrillationInternal medicineOrthostatic vital signsCardiologyVasovagal syncopeBlood pressure

Abstract

fetched live from OpenAlex

Importance: Syncope can result from a reduction in cardiac output from serious cardiac conditions, such as arrhythmias or structural heart disease (cardiac syncope), or other causes, such as vasovagal syncope or orthostatic hypotension. Objective: To perform a systematic review of studies of the accuracy of the clinical examination for identifying patients with cardiac syncope. Study Selection: Studies of adults presenting to primary care, emergency departments, or referred to specialty clinics. Data Extraction and Synthesis: Relevant data were abstracted from articles in databases through April 9, 2019, and methodologic quality was assessed. Included studies had an independent comparison to a reference standard. Main Outcomes and Measures: Sensitivity, specificity, and likelihood ratios (LRs). Results: Eleven studies of cardiac syncope (N = 4317) were included. Age at first syncope of at least 35 years was associated with greater likelihood of cardiac syncope (n = 323; sensitivity, 91% [95% CI, 85%-97%]; specificity, 72% [95% CI, 66%-78%]; LR, 3.3 [95% CI, 2.6-4.1]), while age younger than 35 years was associated with a lower likelihood (LR, 0.13 [95% CI, 0.06-0.25]). A history of atrial fibrillation or flutter (n = 323; sensitivity, 13% [95% CI, 6%-20%]; specificity, 98% [95% CI, 96%-100%]; LR, 7.3 [95% CI, 2.4-22]), or known severe structural heart disease (n = 222; range of sensitivity, 35%-51%, range of specificity, 84%-93%; range of LR, 3.3-4.8; 2 studies) were associated with greater likelihood of cardiac syncope. Symptoms prior to syncope that were associated with lower likelihood of cardiac syncope were mood change or prodromal preoccupation with details (n = 323; sensitivity, 2% [95% CI, 0%-5%]; specificity, 76% [95% CI, 71%-81%]; LR, 0.09 [95% CI, 0.02-0.38]), feeling cold (n = 412; sensitivity, 2% [95% CI, 0%-5%]; specificity, 89% [95% CI, 85%-93%]; LR, 0.16 [95% CI, 0.06-0.64]), or headache (n = 323; sensitivity, 3% [95% CI, 0%-7%]; specificity, 80% [95% CI, 75%-85%]; LR, 0.17 [95% CI, 0.06-0.55]). Cyanosis witnessed during the episode was associated with higher likelihood of cardiac syncope (n = 323; sensitivity, 8% [95% CI, 2%-14%]; specificity, 99% [95% CI, 98%-100%]; LR, 6.2 [95% CI, 1.6-24]). Mood changes after syncope (n = 323; sensitivity, 3% [95% CI, 0%-7%]; specificity, 83% [95% CI, 78%-88%]; LR, 0.21 [95% CI, 0.06-0.65]) and inability to remember behavior prior to syncope (n = 323; sensitivity, 5% [95% CI, 0%-9%]; specificity, 82% [95% CI, 77%-87%]; LR, 0.25, [95% CI, 0.09-0.69]) were associated with lower likelihood of cardiac syncope. Two studies prospectively validated the accuracy of the multivariable Evaluation of Guidelines in Syncope Study (EGSYS) score, which is based on 6 clinical variables. An EGSYS score of less than 3 was associated with lower likelihood of cardiac syncope (n = 456; range of sensitivity, 89%-91%, range of specificity, 69%-73%; range of LR, 0.12-0.17; 2 studies). Cardiac biomarkers show promising diagnostic accuracy for cardiac syncope, but diagnostic thresholds require validation. Conclusions and Relevance: The clinical examination, including the electrocardiogram as part of multivariable scores, can accurately identify patients with and without cardiac syncope.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.006
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.293
Teacher spread0.265 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreReview

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

Citations43
Published2019
Admission routes1
Has abstractyes

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