Transcranial Doppler versus transthoracic echocardiography for the detection of patent foramen ovale in patients with cryptogenic cerebral ischemia: A systematic review and diagnostic test accuracy meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Patent foramen ovale (PFO) can be detected in up to 43% of patients with cryptogenic cerebral ischemia undergoing investigation with transesophageal echocardiography (TEE). The diagnostic value of transthoracic echocardiography (TTE) in the detection of PFO in patients with cryptogenic ischemic stroke or transient ischemic attack has not been compared with that of transcranial Doppler (TCD) using a comprehensive meta-analytical approach. METHODS: We performed a systematic literature review to identify all prospective observational studies of patients with cryptogenic cerebral ischemia that provided both sensitivity and specificity measures of TTE, TCD, or both compared to the gold standard of TEE. RESULTS: Our literature search identified 35 eligible studies including 3,067 patients. The pooled sensitivity and specificity for TCD was 96.1% (95% confidence interval [CI] = 93.0-97.8%) and 92.4% (95% CI = 85.5-96.1%), whereas the respective measures for TTE were 45.1% (95% CI = 30.8-60.3%) and 99.6% (95% CI = 96.5-99.9%). TTE was superior in terms of higher positive likelihood ratio values (LR+ = 106.61, 95% CI = 15.09-753.30 for TTE vs LR+ = 12.62, 95% CI = 6.52-24.43 for TCD; p = 0.043), whereas TCD demonstrated lower negative likelihood values (LR- = 0.04, 95% CI = 0.02-0.08) compared to TTE (LR- = 0.55, 95% CI = 0.42-0.72; p < 0.001). Finally, the area under the summary receiver operating curve (AUC) was significantly greater (p < 0.001) in TCD (AUC = 0.98, 95% CI = 0.97-0.99) compared to TTE studies (AUC = 0.86, 95% CI = 0.82-0.89). INTERPRETATION: TCD is more sensitive but less specific compared to TTE for the detection of PFO in patients with cryptogenic cerebral ischemia. The overall diagnostic yield of TCD appears to outweigh that of TTE.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".