All-cause mortality and survival in adults with 22q11.2 deletion syndrome
Bibliographic record
Abstract
PURPOSE: Given limited data available on long-term outcomes in 22q11.2 deletion syndrome (22q11.2DS), we investigated mortality risk in adults with this microdeletion syndrome. METHODS: We studied 309 well-characterized adults (age ≥17 years) with 22q11.2DS and their 1014 unaffected parents and siblings, using a prospective case-control design. We used Cox proportional hazards regression modeling and Kaplan-Meier curves to investigate effects of the 22q11.2 deletion and its associated features on all-cause mortality and survival. RESULTS: The 22q11.2 deletion (hazard ratio [HR] 8.86, 95% CI 2.87-27.37) and major congenital heart disease (CHD; HR 5.03, 95% CI 2.27-11.17), but not intellectual disability or psychotic illness, were significant independent predictors of mortality for adults with 22q11.2DS compared with their siblings. Amongst those with 22q11.2DS, there were 31 deaths that occurred at a median age of 46.4 (range 18.1-68.6) years; a substantial minority had outlived both parents. Probability of survival to age 45 years was approximately 72% for those with major CHD, and 95% for those with no major CHD (p < 0.0001). CONCLUSION: For adults with 22q11.2DS, the 22q11.2 deletion and more severe forms of CHD both contribute to a lower life expectancy than family-based expectations. The results have implications for genetic counseling and anticipatory care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".