Cross‐Sectional Echocardiographic Assessment of Atrioventricular Septal Defect: Basic Morphology and Preoperative Risk Factors
Bibliographic record
Abstract
Accurate evaluation of an atrioventricular septal defect is readily achieved by echocardiography. A sound understanding of the basic morphology and associated lesions is key to this approach. This article first details the features that are common to all hearts with an atrioventricular septal defect, irrespective of the presence or absence of an interatrial or interventricular communication. These common features are: (1) inlet outlet disproportion; (2) absence of the atrioventricular muscular septum; (3) abnormal position of the left ventricular papillary muscles; (4) abnormal configuration of the atrioventricular valves and, (5) cleft in the left atrioventricular valve. These are all predicated by a sprung atrioventricular junction. Second, is a detailed outline of the associated risk factors that must be identified by the echocardiographer prior to presenting the patient for surgical management, with the most important ones being abnormalities of the left atrioventricular valve and left ventricular outflow tract obstruction. Indeed, in this current era it is rarely necessary to perform other investigations prior to surgical repair.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".