Bibliographic record
Abstract
Residents of nursing homes are at high risk of infectious complications due to respiratory infection. The nursing home setting places residents at high risk given the frequent contact among residents and by staff along with the possibility for continuous introduction of respiratory viruses from the community. Nursing home residents are among the most frail members of society. They have multiple comorbidities that can increase their risk of infection. Immunosenescence plays an important role in not only rendering these seniors susceptible in infection, particularly viral respiratory infection, but also interferes with protection. That is, the ability to mount a robust immune response to influenza and pneumococcal vaccine increase the risk. There have been a number of T cell deficits described in this population. CD4+ T cells, in particular T-regs and CMV-reactive CD4+ T cells, have been shown to be predictive of respiratory viral infection in this population. Although evidence exists that T cell subsets may correlate better with response to vaccine and protection, antibody responses to influenza vaccine remains an important correlate in this population. Large-scale epidemiologic studies are needed to establish better correlation between biomarkers for protection and respiratory and other pathogens that circulate in nursing homes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".