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Record W3146878631 · doi:10.1097/coh.0000000000000677

Frailty: the current challenge for aging people with HIV

2021· review· en· W3146878631 on OpenAlexaff
Julian Falutz, Fátima Brañas, Kristine M. Erlandson

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2021
Typereview
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Current (fluid)GerontologyMEDLINEMedicineData scienceComputer scienceFamily medicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Older adults account for the majority of people with HIV (PWH) in high-income countries and have increasingly complex clinical profiles related to premature aging. Frailty is an important geriatric syndrome affecting a minority of PHW. Frailty negatively affects PHW's clinical status and quality of life. This review will update care providers on the current state of frailty that limits the healthspan of PWH. RECENT FINDINGS: Ongoing low-level HIV replication in treated PWH leads to immune activation and chronic inflammation contributing to the destabilization of normally autoregulated physiologic systems in response to environmental and biologic challenges characteristic of frailty. Understanding these underlying mechanisms will determine potential intervention options. Potentially reversible risk factors that promote progression to and reversion from the dynamic state of frailty are being studied and will help prevent frailty. Simple assessment tools and treatment strategies for frailty are being adapted for aging PWH. SUMMARY: Insight into underlying biologic mechanisms and adapting proven geriatric principles of interdisciplinary care will inform the healthy aging of PWH.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.145
GPT teacher head0.440
Teacher spread0.295 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations36
Published2021
Admission routes1
Has abstractyes

Explore more

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