Bibliographic record
Abstract
The US Census Bureau predicts that 20% of the US population will be 65 years or older by 2030 (1,2). In 2019, the United States had 8.6 geriatricians for every 100,000 people (3). Given this shortage, educating health care professionals on the unique aspects of aging-related care is essential. Within our current health care system, there is an emphasis on disease-specific management, which may lead to the under recognition of geriatric syndromes such as frailty, delirium, and falls—conditions that significantly impact functional status, quality of life, and mortality. A geriatric syndrome is a clinical condition in older adults that does not fit into a discrete disease category but is characterized by the inability of the body to compensate and overcome cumulative impairments in multiple systems (4). In 2017, geriatricians in Canada and the United States officially launched the Geriatric 5 Ms framework, which focuses on the following key areas: mind, mobility, medications, what matters most, and multicomplexity (5). As individuals age, their gastrointestinal (GI) system undergoes physiological changes that can increase susceptibility to common GI disorders. One of the most notable changes is the slowing of digestive processes, which can contribute to indigestion, bloating, constipation, and altered nutrient absorption. Reduced motility in the intestines can further exacerbate issues such as constipation, irritable bowel syndrome (IBS), and diverticulosis. Additionally, the aging liver and pancreas often produce fewer digestive enzymes, which can impair nutrient absorption and contribute to malnutrition or deficiencies in key vitamins and minerals. The aging process also increases the risk of more serious GI issues, such as gastroesophageal reflux disease (GERD) and colorectal cancer. In older individuals, the lower esophageal sphincter may weaken, making it easier for stomach acid to flow back into the esophagus, leading to GERD. Moreover, the risk of colorectal cancer increases significantly after the age of 50, with regular screenings becoming crucial for early detection. In terms of treatment, older patients often have multiple comorbidities and are on various medications, which can exacerbate GI issues. Medications such as NSAIDs or certain antihypertensives can irritate the stomach lining or affect bowel movements, further complicating the management of GI disorders in this age group. Despite these challenges, ageism in clinical decision-making remains a significant barrier, with older adults often remaining undertreated. To address these issues, this monograph explores 7 key areas at the intersection of aging and gastroenterology: colonic conditions, inflammatory bowel disease, vaccinations, functional GI disorders, pancreatic diseases, hepatobiliary disorders, and gastroesophageal conditions. By providing an in-depth review of these topics, we aim to equip practitioners with the knowledge needed to navigate the complexities of GI care in older adults, reduce disparities in treatment, and optimize patient outcomes. On behalf of all authors and editors of this Monograph, we would like to acknowledge and give a special thank you to Dr. Seymour Katz, whose vision and dedication inspired this important initiative. We would also like to thank David Stein for his pivotal role in securing funding, Maddie Kachurak for tirelessly and expertly guiding the process, as well as the entire ACG staff for their invaluable support in bringing this work to fruition.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.477 | 0.300 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".