Risk factors for loss to follow-up among children and young adults with congenital heart disease
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors for loss to cardiology follow-up among children and young adults with congenital heart disease. METHODS: We used a matched case-control design. Cases were born before January, 2001 with moderate or complex congenital heart disease and were previously followed up in the paediatric or adult cardiology clinic, but not seen for 3 years or longer. Controls had been seen within 3 years. Controls were matched 3:1 to cases by year of birth and congenital heart disease lesion. Medical records were reviewed for potential risk factors for loss to follow-up. A subset of cases and controls participated in recorded telephone interviews. RESULTS: A total of 74 cases (66% male) were compared with 222 controls (61% male). A history of missed cardiology appointments was predictive of loss to follow-up for 3 years or longer (odds ratio 13.0, 95% confidence interval 3.3-51.7). Variables protective from loss to follow-up were higher family income (odds ratio 0.87 per $10,000 increase, 0.77-0.98), cardiac catheterisation within 5 years (odds ratio 0.2, 95% confidence interval 0.1-0.6), and chart documentation of the need for cardiology follow-up (odds ratio 0.4, 95% confidence interval 0.2-0.8). Cases lacked awareness of the importance of follow-up and identified primary care physicians as their primary source of information about the heart, rather than cardiologists. Unlike cases, controls had methods to remember appointments. CONCLUSIONS: A history of one or more missed cardiology appointments predicted loss to follow-up for 3 or more years, as did lack of awareness of the need for follow-up. Higher family income, recent catheterisations, and medical record documentation of the need for follow-up were protective.
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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 | 0.006 |
| 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 | 0.001 | 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".