Identification de facteurs reliés à une réadmission précoce en soins intensifs pédiatriques après une chirurgie cardiaque
Bibliographic record
Abstract
The postoperative management of children who have undergone cardiac surgery is a critical period requiring close monitoring to ensure optimal recovery. Despite advances in surgical techniques and perioperative management, some children develop complications leading to unplanned early readmission to the Pediatric Intensive Care Unit (PICU). Such readmissions are associated with increased morbidity, prolonged hospitalization, and greater use of healthcare resources. Understanding the factors contributing to these readmissions is essential for improving postoperative care and resource allocation, all with the goal of optimizing patient recovery. This thesis explores the issue of unplanned early readmissions to the PICU after cardiac surgery in children. It includes an article in which we examined the characteristics of unplanned early readmissions to the PICU among children who underwent cardiac surgery in a quaternary Canadian pediatric hospital. This study analyzed patient data within this population to identify factors potentially associated with these readmissions, providing insight into clinical, pre-, peri-, and postoperative variables that may increase this risk. In this study, postoperative single-ventricle physiology was identified as an independent risk factor for readmission to the PICU (OR 8.57 [1.89-46.50]). Additional factors, including a diagnosis of arrhythmia or the requirement for vasopressors prior to surgery, postoperative ventricular dysfunction, and the presence of a pleural effusion at the time of PICU discharge, demonstrated a trend toward association with an increased risk of readmission; however, these did not reach statistical significance after multivariate analysis. By highlighting these factors, this study serves as a foundation for future interventions aimed at reducing the frequency of unplanned readmissions and improving postoperative care for pediatric cardiac surgery patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".