Bibliographic record
Abstract
The global population is aging, and with this demographic shift, the incidence and prevalence of dementia are expected to increase. According to the World Health Organization, an estimated 50 million people are living with dementia worldwide, and this number is expected to triple to 150 million by 2050. Dementia initially affects the brain, eventually affecting the entire body culminating in death, commonly from the complications and comorbidities. People with dementia often experience eating difficulties in addition to a severe decline in cognitive, verbal, and functional abilities secondary to gradual neurodegenerative process, leading to weight loss, malnutrition, and dehydration. When eating difficulties and weight loss occur, health care providers and families often feel obligated to decide to either continue the oral feeding or opt for feeding tube placement. Primary care clinicians, both nurse practitioners and physicians, are presented with challenges when facilitating the decision regarding the feeding options in patients with advanced dementia. This narrative review aims at evaluating the impact of enteral nutrition versus oral feeding by comparing the rates of survival and adverse events in older adults with advanced dementia. It also highlights the best approaches to optimizing nutrition for this frail population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".