Does young age really put the heart at risk?
Bibliographic record
Abstract
Despite significant advances in the management and treatment of heart disease in children, there continue to be patients who have worse outcomes than might be expected. A number of risk factors that could be responsible have been identified. Evidence-based findings will be reviewed, including whether young age and (or) reduced body weight exacerbate these responses. For example, newborn children undergoing congenital cardiac surgery are known to have worse outcomes than older children. Evidence exists that newborn hearts do not tolerate ischemia as well as adult hearts, developing irreversible injury sooner and exhibiting at-risk metabolic profiles. As well, in response to the administration of heparin, elevations in free fatty acids occur during congenital heart surgery in children, which can have detrimental effects on the heart. Furthermore, myocardial energetic state has also been suggested to impact outcomes. Unfavourable energetic profiles were correlated to lower body weights in the same age healthy newborn piglet model. Newborn children suffering from congenital heart disease, with lower body weights, also had lower myocardial energetic state and this correlated with longer postoperative ventilatory support as well as a trend to longer intensive care unit stay. These findings imply that unfavourable myocardial metabolic profiles could contribute to postoperative complications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".