The Prevention of Infections in Older Adults: Vaccination
Bibliographic record
Abstract
All living beings are at risk for experiencing infections; humans are no exception. The prestige and credibility of modern medicine is built in large part on achievements in preventing and treating infectious diseases. For most of human history, there was little that could be done to prevent and treat infections. Millions of humans, of all ages, have died from infections; and in some parts of the world, infection-related deaths remain common. Advances in preventing and treating infectious diseases include improved sanitization, sterilization, pasteurization, immunization, and antibiotics. Vaccination has played a major role in the prevention of lethal diseases, such as smallpox, diphtheria, cholera, and influenza. Because of developing or waning immune function, the young and the old are at particularly high risk of experiencing infections. Influenza and pneumonia remain common causes of death in older adults. Influenza, in particular, has the potential to result in premature mortality for all age groups, including those who are older and particularly those who live in congregate settings. Vaccination is important in promoting healthy aging. J Am Geriatr Soc 68:207-214, 2019.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".