Bibliographic record
Abstract
THE PROPORTION OF OLDER PEOPLE in the general population is steadily increasing worldwide, with the most rapid growth in low- and middle-income countries (27). This demographic change is to be celebrated, because it is the consequence of socioeconomic development and better life expectancy. However, population aging also has important implications for society, in diverse areas including health systems, labor markets, public policy, social programs, and family dynamics (26). A successful response to the aging population will require capitalizing on the opportunities that this transition offers, as well as effectively addressing its challenges. Chronic kidney disease (CKD) is an important public health problem that is characterized by poor health outcomes and very high health care costs. CKD is a major risk multiplier in patients with diabetes, hypertension, heart disease, and stroke, all of which are key causes of death and disability in older people (5). Since the prevalence of CKD is higher in older people, the health impact of population aging will depend in part on how the kidney community responds. March 13, 2014, will mark the celebration of the 9 th World Kidney Day (WKD), an annual event jointly sponsored by the International Society of Nephrology and the International Federation of Kidney Foundations. Since its inception in 2006, WKD has become the most successful effort to raise awareness among policymakers and the general public about the importance of kidney disease. The topic for WKD 2014 is “CKD in older people.” This article reviews the key links between kidney function, age, health, and illness and discusses the implications of the aging population for the care of people with CKD. Epidemiology of Aging The key drivers of population aging are socioeconomic development and increasing prosperity, which result in lower perinatal, infant and childhood mortality; lower risk of death in early adulthood due to accidents and unsafe living conditions; and improving survival of middle-aged and older people due to chronic disease. The resulting increases in life expectancy (together with the lower birth rates that typically accompany socioeconomic development) mean that older people account for a larger proportion of the general population (27). The extent of the resulting changes in population characteristics can be startling, especially for developing countries (Fig. 1). In contrast to the situation even two generations ago, people can expect to live for many years after the usual retirement age. For example, UK men and women aged 65 yr in 2030 can expect to live until age 88 and 91 years, respectively (22). Predicted life expectancy for today’s children is controversial, but experts estimate that 50% of UK children born in 2007 will live to at least 103 yr (22). Although it is clear that people are living longer, it is uncertain how much of the increased life expectancy will translate into years of good health. These demographic changes have dramatic potential implications for conditions such as CKD, for which the prevalence increases with age.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".