Broadening the Healthy Aging Paradigm: Inclusion of Gestation, Development, and Reproductive Health
Bibliographic record
Abstract
The prevailing focus of lifespan health research has predominantly centered on "healthy aging". This oversight may hinder the understanding of health across the lifespan, as disorders in earlier stages can substantially impact overall health and longevity. Aging, conceptually, begins at gestation. The trajectory of an individual's health is influenced from the earliest stages of life, where adverse conditions can set a foundation for lifelong health challenges. For example, suboptimal conditions during gestation leading to premature birth can predispose individuals to various health issues later in life. Additionally, precocious puberty defined as the onset of sexual maturity before eight years of age or early menopause-occurring before 50 years of age requires medical intervention and is indicative of atypical aging processes. To address these critical gaps in lifespan health research, the expansion of medical lexicons and research categorizations is advocated to include "healthy gestation," "healthy development," and "healthy reproduction" alongside "healthy aging." This broader terminology will enable a more comprehensive investigation of disorders at all life stages. An integrative approach underscores the interconnectedness of all life stages and the continuous nature of aging, advocating for a seamless continuum in health research and interventions from gestation through late adulthood.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".