Mortality and Causes of Death in the Familial Intracranial Aneurysm Study
Bibliographic record
Abstract
BACKGROUND: Higher mortality for patients with aneurysmal subarachnoid haemorrhage has been reported. AIMS: In families with intracranial aneurysms, we sought to determine whether mortality among subjects with intracranial aneurysm (affected) was higher and related to rupture, compared with unaffected family members. METHODS: Subjects enrolled in the Familial Intracranial Aneurysm protocol were contacted yearly and their status was obtained. If reported to be deceased, the cause of death was verified by available records. A Cox proportional hazards model was utilized to compare mortality rates. RESULTS: Of the 2794 subjects, 1073 were affected and 1721 were unaffected. There were 8525 person-years of follow-up (mean 3·05 ± 1·73 years) and 85 deaths. Age at study entry for the affected (58·4 ± 11·9 years) was significantly older (P < 0·0001) than for the unaffected (52·2 ± 16·1). After adjusting for age, the overall mortality rate for the affected subjects was not significantly different from that for the unaffected (Rate Ratio [RR] 1·26, 95% confidence interval 0·82-1·93, P = 0·292). There was a strong effect modification due to age. The mortality rate ratio of the affected to the unaffected who were ≤60 years of age was RR = 3·48 (95% confidence interval: 1·59-7·63, P = 0·002), the rate for the affected subjects who were ≥60 was less than the rate for the unaffected (RR = 0·69, 95% confidence interval: 0·404-1·19, P = 0·178). The affected who had ruptures had 2·62 times the mortality rate as those without ruptures (95% confidence interval 1·43-4·80, P = 0·002). CONCLUSION: The overall mortality was similar for the affected and unaffected subjects in this cohort. Among the affected only, those with ruptured intracranial aneurysm had a higher mortality rate than those without ruptured.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".