Hypoplastic Left Heart Syndrome: Diagnosis, Care and Management From Fetal Life and Beyond
Bibliographic record
Abstract
Hypoplastic left heart syndrome (HLHS) accounts for 2% to 3% of all congenital heart disease but is responsible for 25% to 40% of all neonatal cardiac deaths. Although the exact genetic origins of HLHS have not been clearly defined, various genetic and chromosomal associations have been identified. Advancements in fetal echocardiography have resulted in accurate diagnosis of congenital heart disease. On the basis of physical examination findings, fetuses may be candidates for prenatal intervention. In general, after prenatal diagnosis of HLHS, parents are faced with 2 choices: termination or continuation of pregnancy. If pregnancy is continued to delivery, patients may choose comfort care, surgical palliation with the Fontan procedure, or transplantation. A once lethal congenital anomaly, HLHS has undergone a marked evolution in management and prognosis during the last several decades. With advancements in prenatal diagnosis, neonatal management, and surgical palliation, patient survival has drastically improved: at an experienced center, current survival rates are very high after the Norwood procedure, with high rates of overall freedom from death or transplantation through 20 years. With survival becoming more promising, the issues that now take precedence are neurodevelopmental outcomes, Fontan procedure complications, and quality of life. Although much progress has been made in caring for this patient population, HLHS remains a high-risk condition that requires lifelong medical follow-up and has significant long-term morbidity, affecting overall quality of life for patients and their families.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".