Intraoperative ST Segment Depression During General Anesthesia in a Child: Early Detection of Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
Continuous electrocardiographic (ECG) monitoring remains crucial during surgery in infants and children. Although generally uncommon in pediatric-aged patients, ECG changes may occasionally be indicative of a variety of myocardial pathologies including anomalous origin of coronary arteries, ventricular hypertrophy, myocarditis, hypothermia, drug effects, electrolyte abnormalities, acid-base disturbances or conduction system disorders such as Wolff-Parkinson-White and Brugada syndrome. Distinguishing between pathologic and non-pathologic conditions impacting the ECG must be considered so that appropriate interventions are provided to prevent perioperative morbidity and mortality. We report a case of a 2-year-old child who exhibited ST segment depression and increased R wave amplitude during general anesthesia. Although the anesthetic care was uneventful and the patient was otherwise asymptomatic, immediate postoperative workup including echocardiogram revealed previously undiagnosed hypertrophic cardiomyopathy. The occurrence of intraoperative ST-T wave changes in this patient underscores the need for a high index of suspicion for underlying cardiac pathology, even in the absence of overt clinical manifestations. This case highlights the importance of intraoperative ECG monitoring in pediatric patients, explores the causes of ST-T wave changes, reviews similar cases in the literature, and proposes a pathway for perioperative evaluation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".