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Record W4318755457 · doi:10.1097/coh.0000000000000777

HIV and chronic lung disease

2022· review· en· W4318755457 on OpenAlexafffund
Janice M. Leung

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2022
Typereview
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsCOPDMedicineLung cancerPopulationHuman immunodeficiency virus (HIV)DiseaseIntensive care medicineLungCancerInternal medicineImmunologyEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: As people living with human immunodeficiency virus (HIV, PLWH) age, aging-related comorbidities have come into focus as major challenges to their overall health. In this review, an in-depth overview of the two most commonly encountered chronic lung diseases in PLWH, chronic obstructive pulmonary disease (COPD) and lung cancer, is provided. RECENT FINDINGS: The risk for both COPD and lung cancer remains significantly higher in PLWH compared to the HIV-uninfected population, although fortunately rates of lung cancer appear to be declining over the last two decades. Outcomes for PLWH with these conditions, though, continue to be poor with worse survival rates in comparison to the general population. PLWH still face major barriers in accessing care for these conditions, including a higher likelihood of being underdiagnosed with COPD and a lower likelihood of being referred for lung cancer screening or treatment. A lack of evidence for optimal treatment strategies for both COPD and lung cancer still hampers the care of PLWH with these conditions. SUMMARY: COPD and lung cancer represent substantial burdens of disease in PLWH. Improved access to standard-of-care screening and treatment and greater investigation into therapeutic responses specifically in this population are recommended.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.379
Teacher spread0.316 · 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 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

Citations9
Published2022
Admission routes2
Has abstractyes

Explore more

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