Bibliographic record
Abstract
The World Health Organization estimates that adults aged 65 years and older will account for one-fifth of the overall population in 34 nations around the world by the end of this decade. India will only cross that landmark in 2050, when its older adult population is expected to be around 347 million people, up from 149 million in 2022.[1] These are staggering numbers and should give us some pause. Although older adults currently account for just under 10% of India’s population, this is still a sizable cohort, equaling the entire population of countries like Russia or Japan. While the demographic changes brought on by an aging population affect all aspects of our lives, it especially prompts a review of our preparedness to provide medical and dental services for a population at higher risk for disease. Aging is all around us. In India, our experiences growing up in multigenerational households are often our first lessons in respect and compassion for older adults. We carry these values into our future roles as dental providers but find that purposeful dental training focusing on older adults and people with special health-care needs varies widely in its intensity and availability. Local economic and cultural factors play an important role in the demand for such care, and changes in family structure, growth in per capita income, and the expansion of the middle class have all influenced how India’s older adults perceive their dental needs. Itis,therefore,criticalthatour future dental providershave the technical and interpersonal skills to provide comprehensive care to older adults and those with special health-care needs. Historically, countries have responded to the challenge of fulfilling the oral health needs of their older adult populations only when this cohort has developed a critical mass. Japan, Canada, USA, and several countries in Europe that host formal training programs in geriatric dentistry also happen to have older adults representing more than 15% of their overall population. The thought here is that the longevity that older adults now enjoy with advances in medical science and general living conditions will also bring with it enhanced oral health-care needs. Similarly, India’s burgeoning older adult population demands short- and long-term investment in curriculum, accommodations, and technology to train our young graduates and specialists in providing care to this group of individuals. All of the opportunities that I got to learn about older adults, be it a dental procedure or simply listening to their thoughts on what they expect from the remaining years of their lives, impacted me greatly. I got to see how India’s older adults were an extremely diverse group that transcended language, region, religion, and socioeconomic status in their desire for health and happiness. The intersection of age and health makes for uncertain outcomes; however, one thing is clear, the number of older adults with cognitive impairments and complex health conditions who also need dental care, be it on a routine or emergency basis, will only continue to grow. In the pages that follow, as you review papers that go into various aspects of oral health science, I ask you to consider how these learnings might benefit older adult patients in your care and think of ways to support the oral health needs of this cohort further.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.053 | 0.021 |
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".