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Record W2052992415 · doi:10.1155/2012/390406

Causes of Death in HIV Patients and the Evolution of an AIDS Hospice: 1988–2008

2012· article· en· W2052992415 on OpenAlexaffabout
Ann Stewart, Soo Chan Carusone, Kent To, Nicole Schaefer-McDaniel, Mark Halman, Richard Grimes

Bibliographic record

VenueAIDS Research and Treatment · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsSt. Michael's HospitalRegent Park Community Health CentreCasey HouseMcMaster UniversityUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesCenter for AIDS Research, University of WashingtonNational Institutes of Health
KeywordsMedicineHuman immunodeficiency virus (HIV)CancerDiseasePalliative careHealth careCause of deathGerontologyFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

This paper reports on the transformation that has occurred in the care of people living with HIV/AIDS in a Toronto Hospice. Casey House opened in the pre-HAART era to care exclusively for people with HIV/AIDS, an incurable disease. At the time, all patients were admitted for palliative care and all deaths were due to AIDS-defining conditions. AIDS-defining malignancies accounted for 22 percent of deaths, mainly, Kaposi sarcoma and lymphoma. In the post-HAART era, AIDS-defining malignancies dropped dramatically and non-AIDS-defining malignancies became a significant cause of death, including liver cancer, lung cancer and gastric cancers. In the post-HAART era, people living with HIV/AIDS served at Casey House have changed considerably, with increasing numbers of patients facing homelessness and mental health issues, including substance use. Casey House offers a picture of the evolving epidemic and provides insight into changes and improvements made in the care of these patients.

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)0.003
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 score0.209
Threshold uncertainty score0.415

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.385
Teacher spread0.336 · 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

Citations29
Published2012
Admission routes2
Has abstractyes

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