T‐Cell Phenotypes Predictive of Frailty and Mortality in Elderly Nursing Home Residents
Bibliographic record
Abstract
Objectives To determine whether immune phenotypes associated with immunosenescence are predictive of frailty and mortality within 1‐year in elderly nursing home residents. Design Cross sectional study of frailty; prospective cohort study of mortality. Setting Thirty‐two nursing homes in four Canadian cities between September 2009 and October 2011. Participants Nursing home residents aged 65 and older (N = 1,072, median age 86, 72% female). Measurements After enrollment, peripheral blood mononuclear cells were obtained and analyzed using flow cytometry for CD 4 + and CD 8 + T‐cell subsets (naïve, memory (central, effector, terminally differentiated, senescent), and regulatory T‐cells) and cytomegalovirus ( CMV )‐reactive CD 4 + and CD 8 + T‐cells. Multilevel linear regression analysis was performed to determine the relationship between immune phenotypes and frailty; frailty was measured at the time of enrollment using the Frailty Index. A Cox proportional hazards model was used to determine the relationship between immune phenotypes and time to death (within 1 year). Results Mean Frailty Index was 0.44 ± 0.13. Multilevel regression analysis showed that higher percentages of naïve CD 4 + T‐cells ( P = .001) and effector memory CD 8 + T‐cells ( P = .02) were associated with a lower mean Frailty Index, whereas a higher percentage of CD 8 + central memory T‐cells was associated with a higher mean Frailty Index score ( P = .02). One hundred fifty one (14%) members of the cohort died within 1 year. Multivariable analysis showed a significant negative multiplicative interaction between age and percentage of CMV ‐reactive CD 4 + T‐cells (hazard ratio = 0.87, 95% confidence interval = 0.79–0.96). No other significant factors were identified. Conclusion Immune phenotypes found to be predictive of frailty and mortality in this study can help further understanding of immunosenescence and may provide a rationale for future intervention studies designed to modulate immunity.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".