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Record W2801158994 · doi:10.1111/jgs.15248

Infectious Diseases in Older Adults of Long‐Term Care Facilities: Update on Approach to Diagnosis and Management

2018· review· en· W2801158994 on OpenAlexaff
Robin Jump, Christopher J. Crnich, Lona Mody, Suzanne Bradley, Lindsay E. Nicolle, Thomas T. Yoshikawa

Bibliographic record

VenueJournal of the American Geriatrics Society · 2018
Typereview
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsUniversity of ManitobaHealth Sciences CentreManitoba Health
FundersNIH Clinical CenterNational Institute on AgingGeriatric Research Education and Clinical CenterAgency for Healthcare Research and QualityU.S. Department of Veterans Affairs
KeywordsMedicineIntensive care medicineClostridium difficileLong-term carePneumoniaHealth careAntimicrobial stewardshipNorovirusAntibioticsNursingPathologyAntibiotic resistanceInternal medicineOutbreak

Abstract

fetched live from OpenAlex

The diagnosis, treatment, and prevention of infectious diseases in older adults in long-term care facilities (LTCFs), particularly nursing facilities, remains a challenge for all health providers who care for this population. This review provides updated information on the currently most important challenges of infectious diseases in LTCFs. With the increasing prescribing of antibiotics in older adults, particularly in LTCFs, the topic of antibiotic stewardship is presented in this review. Following this discussion, salient points on clinical relevance, clinical presentation, diagnostic approach, therapy, and prevention are discussed for skin and soft tissue infections, infectious diarrhea (Clostridium difficile and norovirus infections), bacterial pneumonia, and urinary tract infection, as well as some of the newer approaches to preventive interventions in the LTCF setting.

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)0.004
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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

Citations107
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Geriatrics SocietySame topicUrinary Tract Infections ManagementFrench-language works237,207