Appropriate use of transthoracic echocardiography in the investigation of general medicine patients presenting with syncope or presyncope
Bibliographic record
Abstract
STUDY PURPOSE: Routine transthoracic echocardiography (TTE) in patients with syncope or presyncope is resource-intensive. We assessed if risk thresholds defined by a validated risk score may identify patients at low risk of cardiac abnormality in whom TTE is unnecessary. STUDY DESIGN: We conducted a retrospective study of all general medicine patients with syncope/presyncope presenting to a tertiary hospital between July 2016 and September 2020 and who underwent TTE. The Canadian Syncope Risk Score (CSRS) was used to categorise patients as low to very low risk (score -3 to 0) or moderate to high risk (score ≥1) for serious adverse events at 30 days. A cut-point of 0 was used to calculate the sensitivity, specificity, positive and negative predictive values (PPV and NPV) for CSRS and the odds ratio (OR) of a clinically significant finding on TTE in patients with CSRS ≥1 compared with all patients. RESULTS: Among 157 patients, the CSRS categorised 69 (44%) as very low to low risk in whom TTE was normal. In 88 patients deemed moderate to high risk, TTE detected a cardiac abnormality in 24 (27%). A CSRS ≥1 yielded a sensitivity of 100% (95% CI 85.7% to 100%), specificity of 51.1% (95% CI 42.3% to 59.8%), PPV of 26.5% (95% CI 26.3% to 30.1%) and NPV of 100% (95% CI 92.5% to 100%) for cardiac abnormalities and doubled the odds of an abnormality (OR = 2.05, 95% CI 1.08 to 3.87, p = 0.028). CONCLUSION: In general medicine patients with syncope/presyncope, using the CSRS to stratify risk of a cardiac abnormality on TTE can almost halve TTE use.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".