Effect of parental age at birth on the accumulation of deficits, frailty and survival in older adults
Bibliographic record
Abstract
INTRODUCTION: parental age at conception may affect life expectancy. Adult daughters of older fathers seem to live shorter lives and, in one study, being born to a mother aged <25 was an important predictor of exceptional longevity. The effect of parental age on fitness/frailty in late life is unknown. We aimed to investigate the relationships between parental age and frailty and longevity in older adults. METHODS: in the Canadian Study of Health and Aging (CSHA), data was collected on individuals aged >or=65 using a Self-Assessed Risk Factor Questionnaire and screening interview. In this secondary analysis, 5112 participants had complete data for parental age, frailty status and 10-year survival. Parental age was divided into three groups, with cut-offs at 25 and 45 for fathers and at 25 and 40 for mothers. Frailty was defined by an index of deficits. Survival was analysed using Kaplan-Meier curves and Cox regression with analyses adjusted for subject's age, sex and age of the other parent. RESULTS: mean maternal age at subject's birth was 29.2y (SD 6.8) and mean paternal age 33.3y (SD 7.8). There was no effect of maternal or paternal age on survival for either sons or daughters. Similarly, there was no association between parental age and subject frailty in old age. CONCLUSION: we did not identify an association between parental age and frailty or longevity in older adult participants in the CSHA.
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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 | 0.008 |
| 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) | 0.002 | 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".