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Record W2532902760 · doi:10.1111/jgs.14507

T‐Cell Phenotypes Predictive of Frailty and Mortality in Elderly Nursing Home Residents

2016· article· en· W2532902760 on OpenAlexafffundabout
Jennie Johnstone, Robin Parsons, Fernando Botelho, Jamie Millar, Shelly McNeil, Tamàs Fülöp, Janet E. McElhaney, Melissa K. Andrew, Stephen D. Walter, P.J. Devereaux, Mehrnoush Malek, Ryan R. Brinkman, Jonathan L. Bramson, Mark Loeb

Bibliographic record

VenueJournal of the American Geriatrics Society · 2016
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsBC Cancer AgencyHealth Sciences NorthDalhousie UniversityNova Scotia Health AuthorityCapital District Health AuthorityUniversity of British ColumbiaPublic Health OntarioMcMaster University Medical CentreUniversité de SherbrookeMcMaster University
FundersNational Institute of Biomedical Imaging and BioengineeringNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of HealthPublic Health Agency
KeywordsMedicineImmunosenescenceHazard ratioProportional hazards modelGerontologyCohortInternal medicineImmune systemImmunologyConfidence interval

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.292
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2016
Admission routes3
Has abstractyes

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