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Record W2940336677 · doi:10.1093/cid/ciy892

Similar Outcomes of<i>Legionella</i>Pneumonia in Treated Human Immunodeficiency Virus Infection

2018· letter· en· W2940336677 on OpenAlexaff
Breanne M. Head, Yoav Keynan

Bibliographic record

VenueClinical Infectious Diseases · 2018
Typeletter
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineHuman immunodeficiency virus (HIV)VirologySidaImmunopathologyViral diseaseImmunology

Abstract

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To the Editor—We read with interest the article by Cillóniz et al [1] on the first retrospective case-control series of Legionella pneumonia among human immunodeficiency virus (HIV)-infected individuals from 3 Spanish hospitals. The report significantly adds to the literature by comparing HIV-infected to HIV-uninfected individuals in terms of Legionella pneumonia disease severity, treatment, and outcomes. Essentially, the pneumonia severity scores, antimicrobial regimens, and need for intensive care admission were not different between cases and controls. The study findings are in keeping with the report on the South Bronx Legionella outbreak, in which 24 of 138 individuals had an underlying diagnosis of HIV, suggesting overrepresentation of HIV among those who became clinically ill with Legionella [2]. The mortality of HIV-infected individuals did not differ from that of non-HIV–infected individuals. In contrast, in a 2016 publication by Wolter et al [3] who looked at Legionella-infected patients with known HIV status (n = 20) from 2 sites in South Africa, HIV-infected individuals had higher case-fatality rates than HIV-uninfected individuals (20% vs 0%) and, unlike in Cillóniz et al, only 24% of Legionella patients were administered appropriate anti-Legionella antibiotic therapy. Although we recognize that this article is a stepping-stone to better understanding Legionella infection in HIV, the series has several limitations that must be acknowledged. As Cillóniz et al stated, HIV infection was well controlled in the majority of their cases, with patients having a median CD4+ T-cell count of 335/μL (interquartile range, 215–500/μL), representing individuals with a relatively conserved immune state. Consequently, due to their intact immune status, as is stipulated by the Morbidity and Mortality Weekly Report and the Infectious Disease Society of America treatment guidelines [4, 5], cases and controls were administered a similar treatment regimen that included appropriate empirical coverage for Legionella, which may explain the low rates of intensive care unit admission and the favorable outcomes seen in these patients. The results may not be applicable to HIV-infected individuals with advanced disease and low CD4 counts. Individuals with CD4+ T-cell counts <200/μL, in whom opportunistic infections such as Pneumocystis jirovecii, Mycobacterium Tuberculosis, or avium complex are of high concern, atypical bacterial pneumonia agents such as Legionella spp. are often not targeted by empirical antimicrobial therapy [6]. Therefore, the findings by Cillóniz et al may be relevant for treated HIV-infected individuals with preserved CD4 T-cell counts but may not hold true for patients who are profoundly immunocompromised but should not be generalized to conclude that the clinical presentation and outcomes do not differ between HIV-infected and non-HIV–uninfected individuals. Potential conflicts of interest. Both authors: No reported conflicts. Both authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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)0.007
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.382
Teacher spread0.330 · 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

Citations2
Published2018
Admission routes1
Has abstractno

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