Early Extubation after Pediatric Cardiac Surgery: Systematic Review, Meta-analysis, and Evidence-Based Recommendations
Bibliographic record
Abstract
OBJECTIVE: To derive evidence-based recommendations regarding early extubation strategy after congenital cardiac surgery. OUTCOMES: Incidence of total mortality, morbidity, reintubation, length, and costs of intensive care unit and hospital stay. EVIDENCE: Medline, Embase, and the Cochrane-controlled trial register on the Cochrane library were searched from the earliest achievable date of each database to present. No language restrictions were applied. Retrieved reprints were evaluated according to a priori inclusion criteria, and those included were critically appraised using established internal validity criteria. BENEFITS AND HARMS: Early extubation (in the operating room or ≤6 hours after surgery) was associated with a lower early mortality. There was a trend toward lower ICU and hospital length of stays, lower hospital costs, and less respiratory morbidity. There was no difference in the rate of reintubation in those extubated early versus late. CONCLUSION: Early extubation appears safe and is associated with reduction in length of ICU and hospital stay without adverse effects on mortality or morbidity. However, studies to date are poor, heterogeneous, and not suitable to determine a causal effect. Therefore, there is need for a well-designed randomized clinical trial to demonstrate the potential significant benefits of early extubation.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".