Incidence and predictors of cardiac catheterisation-related arterial thrombosis in children
Bibliographic record
Abstract
OBJECTIVE: Arterial thrombosis is one of the most reported complications of cardiac catheterisation (CC) in children. The aim of the study was to evaluate the incidence and predictors of arterial thrombosis in children with cardiac diseases (CDs). METHODS: During 12 consecutive months, all children aged 0-19 years undergoing CC of the femoral arteries were included in this observational study. After CC, clinical evaluation of impaired limb perfusion was performed according to local guidelines. Doppler ultrasonography was performed when decreased limb perfusion was suspected. RESULTS: 123 children (30% aged <12 months, 70% aged >12 months) underwent CC. Arterial thrombosis occurred in 14 of the 123 children (11.4%). Twelve cases (12/14=86%) of arterial thrombosis occurred in infants aged <12 months and 2 (2/14=14%) in older children. Overall younger age (p<0.01, OR (95% CI) 0.49 (0.28 to 0.86)) and low body weight (p<0.004, OR (95% CI) 0.78 (0.65 to 0.92)) were significantly associated with an increased risk of arterial thrombosis. Cyanotic CD (p=0.07, OR (95% CI) 2.87 (0.90 to 9.15)) showed a trend towards increased thrombotic risk. CONCLUSIONS: Arterial thrombosis is a common complication of CC in infants. Diagnosis of CC-related arterial thrombosis remains a challenge. Well-defined clinical monitoring protocols may be valuable methods for timely detection and treatment of arterial thrombosis.
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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) | 0.000 | 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 | 0.001 |
| 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".