Cardiac computed tomography and magnetic resonance imaging vs. transoesophageal echocardiography for diagnosing left atrial appendage thrombi
Bibliographic record
Abstract
AIMS: Transoesophageal echocardiography (TOE) is the gold standard for identification of left atrial appendage (LAA) thrombi. However, TOE is semi-invasive and cannot be performed in certain patients. Left atrial appendage thrombi can also be identified by cardiac computed tomography (CCT) and cardiac magnetic resonance imaging (CMR); however, the diagnostic performance of these techniques vs. TOE is unclear. METHODS AND RESULTS: We performed a systematic review and meta-analysis of 22 CCT and 4 CMR studies comparing diagnostic performance to TOE for identification of LAA thrombi. Meta-regression was performed to determine whether expected sensitivity and specificity differed between early and delayed image acquisition protocols for CCT vs. TOE and between CCT and CMR. Cardiac computed tomography demonstrated sensitivity and specificity of 0.99 [confidence interval (CI 0.93-1.00)] and 0.94 (CI 0.90-0.97) respectively vs. TOE. A subgroup analysis comparing early vs. delayed protocol CCT imaging was performed showing no significant differences in sensitivity (P-value = 0.17) however improved specificity of the delayed imaging protocols (P-value = 0.04). Cardiac magnetic resonance imaging demonstrated sensitivity and specificity of 0.80 (CI 0.63-0.91) and 0.98 (CI 0.97-0.99), respectively when compared to TOE. There was no significant difference in sensitivity or specificity between CMR and CCT (P-values 0.996 and 0.484, respectively). CONCLUSION: Cardiac computed tomography and CMR had good to excellent sensitivity and specificity vs. TOE. Further, there was no significant difference in the sensitivity and specificity of CCT vs. CMR, suggesting that both modalities can be considered reasonable alternatives to TOE in the identification of LAA thrombi. Cardiac magnetic resonance imaging may be especially beneficial when TOE and CCT cannot be performed.
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 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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.028 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".