Hypoplastic Left Heart: Management Challenges in Resource-Limited Settings
Bibliographic record
Abstract
Introduction: The management of neonatal single ventricle physiology is challenging, particularly in resource-limited settings. Limitations in infrastructure and specialized health care resources pose significant barriers. Decision-making is further complicated by high postoperative mortality and financial burden. We report the first case of stage I palliation (S1P) for hypoplastic left heart (HLH) in Tripoli, Libya. Case Description: A term female neonate (3.0 kg) was referred on day 4 of life with critical aortic stenosis on prostaglandin infusion. Echocardiography demonstrated normal segmental anatomy with a hypoplastic mitral valve, non-apex-forming hypoplastic left ventricle with endocardial fibroelastosis, severe dysfunction, and critical aortic stenosis. She developed rising oxygen saturations, suggestive of pulmonary overcirculation, prompting expedited surgery. She underwent a Damus–Kaye–Stansel procedure with a 4-mm Sano conduit, atrial septectomy, and duct ligation. Postoperatively, complications included cardiac tamponade, low cardiac output syndrome, pulmonary overcirculation, necrotizing enterocolitis, and sternal wound infection. Discussion: Despite significant morbidities, the patient stabilized with balanced circulation and adequate early growth and was discharged home. The case highlighted the substantial financial burden, as the family relocated to Tripoli and hired a private nurse. S1P for HLH carries high mortality even in high-resource settings. This first institutional experience underscores both the feasibility and challenges of introducing complex congenital surgery programs in resource-limited environments. Conclusion: This case highlights the complexity of S1P in resource-limited settings. Despite complications and financial strain, stabilization and discharge were achieved, demonstrating feasibility in carefully selected patients and the need for ongoing institutional development and capacity building. Publication History Article published online: 31 December 2025 © 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/) Thieme Medical and Scientific Publishers Pvt. Ltd. A-12, 2nd Floor, Sector 2, Noida-201301 UP, India
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".