Risk Factors for Pericardiocentesis After Paediatric Cardiac Surgery
Bibliographic record
Abstract
Background Pericardial effusions are common following pediatric cardiac surgery and can lead to cardiac tamponade in a small minority. However, it is difficult to predict which patients with an effusion will require pericardiocentesis. Therefore, among children with a postoperative effusion, we sought to identify risk factors for requiring pericardiocentesis. Methods We conducted a case-control study including pediatric patients who underwent cardiac surgery between January 1, 2005, and July 1, 2020, at the Stollery Children's Hospital. Cases were defined as those who underwent pericardiocentesis within two months of cardiac surgery and were compared to controls who had an effusion but did not require pericardiocentesis. Controls were matched 2:1 to cases based on age and year of surgery. Results There were 42 cases and 84 controls. Median age at surgery was 3.0 years (interquartile range 0.5-6.4) among cases and 2.2 years (IQR 0.4-5.8) among controls. Median weight at surgery was 13.5 kg (IQR 6.4-18.0) among cases and 13.5 kg (IQR 4.9-23.1) among controls. The use of anticoagulation or antiplatelet agents (OR 3.6, 95% CI 1.5-8.2, p <0.01) in the post-operative period was independently associated with effusions requiring drainage. The use of prednisone postoperatively (OR 3.3, 95% CI 0.8-14.0, p=0.10) and a history of previous pericardial effusion (OR 4.7, 95% CI 0.9-25.6, p=0.08) were associated with a higher odds of pericardiocentesis but did not reach statistical significance. Conclusions Use of postoperative anticoagulation was independently associated with the need for pericardiocentesis. Type of surgical procedure was not associated with the need for drainage.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".