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Record W2893193517 · doi:10.24875/aidsrev.18000002

Chronic Lung Disease in HIV Patients

2018· review· en· W2893193517 on OpenAlexaff
Antonella Santoro, Giovanni Guaraldi, Giulia Besutti, Janice M. Leung, Leonardo M. Fabbri, Simone Neri

Bibliographic record

VenueAids Reviews · 2018
Typereview
Languageen
FieldMedicine
TopicPneumocystis jirovecii pneumonia detection and treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineCOPDComorbidityContext (archaeology)SpirometryDiseaseObstructive lung diseaseLungIntensive care medicineHuman immunodeficiency virus (HIV)ImmunologyInternal medicineAsthma

Abstract

fetched live from OpenAlex

This narrative review discusses literature on chronic obstructive pulmonary disease (COPD) in people living with HIV (PLWH). Existing data indicate that HIV itself, independent of smoking, constitutes a pathogenic agent implicated in this disease condition. COPD can be viewed not exclusively as a pulmonary disease but rather as a systemic syndrome sparked and fueled by a persistent low-grade HIV-attributable inflammatory state. We speculate that even in the absence of airflow obstruction on spirometry, HIV-related lung disease can manifest with respiratory symptoms and structural lung derangement. Although not fully satisfying the global initiative for obstructive lung disease criteria for COPD, this phenotype of small airways lung disease is related to significant impairment of lung health and is associated with a high comorbidity burden. Within the specific context of the aging epidemic affecting HIV patients characterized by a high burden of comorbidities, frailty, and disabilities HIV-related lung disease has to be fit into the framework of the general comorbidity burden that PLWH experience, due to both HIV infection and to incidental HIV-unrelated risk factors. In this review, we will also provide a list of research gaps and an agenda for future studies in HIV 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.005

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 head0.040
GPT teacher head0.353
Teacher spread0.313 · 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 teacher head, not a consensus.

Study designOther design
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

Citations16
Published2018
Admission routes1
Has abstractyes

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