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Record W7099991734

Complex care needs of patients with late-stage HIV disease: A retrospective study

2013· article· en· W7099991734 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioactive Natural Diterpenoids Research
Canadian institutionsnot available
Fundersnot available
KeywordsVenn diagramRetrospective cohort studyPsychological interventionMedical recordPopulationHealth careHuman immunodeficiency virus (HIV)
DOInot available

Abstract

fetched live from OpenAlex

This retrospective chart review provides a profile of an emerging population of vulnerable HIV patients with complex comorbidities. Data were abstracted from all 83 patients admitted in 2008 to Casey House, a community-based hospital dedicated to supportive and palliative care for persons with HIV in Toronto, Canada. We describe patient characteristics, including medical and psychiatric conditions, and use a Venn diagram and case study to illustrate the frequency and reality of co-occurring conditions that contribute to the complexity of patients ’ health and health care needs. The mean age at admission was 49.2 years (SD10.5). Sixty-seven patients (80.7%) were male. Patients experienced a mean of 5.9 medical comorbidities (SD2.3) and 1.9 psychiatric disorders (lifetime Axis I diagnoses). Forty patients (48.2%) experienced cognitive impairment including HIV-associated dementia. Patients were on a mean of 11.5 (SD5.3) medications at admission; 74.7 % were on antiretroviral medications with 55.0 % reporting full adherence. Current alcohol and drug use was common with 50.6 % reporting active use at admission. Our Venn diagram illustrates the breadth of complexity in the clients with 8.4 % of clients living in unstable housing with three or more medical comorbidities and two or more psychiatric diagnoses. Comprehensive HIV program planning should include interventions that can flexibly adapt to meet the multidimensional and complex needs of this segment of patients. Researchers, policy-makers, and clinicians need to have greater awareness of overlapping medical,

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.010
GPT teacher head0.265
Teacher spread0.256 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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