Surveillance of Pediatric Cardiac Surgical Outcome Using Risk Stratifications at a Tertiary Care Center in Thailand
Bibliographic record
Abstract
Objectives. To determine in-hospital mortality and complications of cardiac surgery in pediatric patients and identify predictors of hospital mortality. Methods. Records of pediatric patients who had undergone cardiac surgery in 2005 were reviewed retrospectively. The risk adjustment for congenital heart surgery (RACHS-1) method, the Aristotle basic complexity score (ABC score), and the Society of Thoracic Surgeons and the European Association for Cardiothoracic Surgery Mortality score (STS-EACTS score) were used as measures. Potential predictors were analyzed by risk analysis. Results. 230 pediatric patients had undergone congenital cardiac surgery. Overall, the mortality discharge was 6.1%. From the ROC curve of the RACHS-1, the ABC level, and the STS-EACTS categories, the validities were determined to be 0.78, 0.74, and 0.67, respectively. Mortality risks were found at the high complexity levels of the three tools, bypass time >85 min, and cross clamp time >60 min. Common morbidities were postoperative pyrexia, bleeding, and pleural effusion. Conclusions. Overall mortality and morbidities were 6.1%. The RACHS-1 method, ABC score, and STS-EACTS score were helpful for risk stratification.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".