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Record W2791888954 · doi:10.1056/nejmoa1710922

Antifungal Combinations for Treatment of Cryptococcal Meningitis in Africa

2018· article· en· W2791888954 on OpenAlexaff
Síle F. Molloy, Cecilia Kanyama, Robert S. Heyderman, Angela Loyse, Charles Kouanfack, Duncan Chanda, Sayoki Mfinanga, Elvis Temfack, Shabir Lakhi, Sokoine Lesikari, Adrienne K Chan, Neil Stone, Newton Kalata, Natasha Karunaharan, Mary Peirse, Jayne Ellis, Chimwemwe Chawinga, Sandrine Sa’a Lontsi, Jean-Gilbert Ndong, Philip Bright, Duncan Lupiya, Tao Chen, John Bradley, Jack Adams, Charles van der Horst, Joep J. van Oosterhout, Victor Sini, Yacouba Njankouo Mapoure, Peter Mwaba, Tihana Bicanic, David G. Lalloo, Duolao Wang, Mina C. Hosseinipour, Olivier Lortholary, Shabbar Jaffar, Thomas S. Harrison

Bibliographic record

VenueNew England Journal of Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Allergy and Infectious DiseasesMedical Research CouncilNational Institutes of HealthAgence Nationale de Recherches sur le Sida et les Hépatites ViralesCenter for AIDS Research, University of WashingtonNational Institute for Health and Care ResearchWellcome Trust
KeywordsCryptococcal meningitisAntifungalCryptococcosisCryptococcus neoformansMeningitisMedicineMicrobiologyBiologyVirologyImmunologyHuman immunodeficiency virus (HIV)PediatricsViral disease

Abstract

fetched live from OpenAlex

BACKGROUND: Cryptococcal meningitis accounts for more than 100,000 human immunodeficiency virus (HIV)-related deaths per year. We tested two treatment strategies that could be more sustainable in Africa than the standard of 2 weeks of amphotericin B plus flucytosine and more effective than the widely used fluconazole monotherapy. METHODS: We randomly assigned HIV-infected adults with cryptococcal meningitis to receive an oral regimen (fluconazole [1200 mg per day] plus flucytosine [100 mg per kilogram of body weight per day] for 2 weeks), 1 week of amphotericin B (1 mg per kilogram per day), or 2 weeks of amphotericin B (1 mg per kilogram per day). Each patient assigned to receive amphotericin B was also randomly assigned to receive fluconazole or flucytosine as a partner drug. After induction treatment, all the patients received fluconazole consolidation therapy and were followed to 10 weeks. RESULTS: A total of 721 patients underwent randomization. Mortality in the oral-regimen, 1-week amphotericin B, and 2-week amphotericin B groups was 18.2% (41 of 225), 21.9% (49 of 224), and 21.4% (49 of 229), respectively, at 2 weeks and was 35.1% (79 of 225), 36.2% (81 of 224), and 39.7% (91 of 229), respectively, at 10 weeks. The upper limit of the one-sided 97.5% confidence interval for the difference in 2-week mortality was 4.2 percentage points for the oral-regimen group versus the 2-week amphotericin B groups and 8.1 percentage points for the 1-week amphotericin B groups versus the 2-week amphotericin B groups, both of which were below the predefined 10-percentage-point noninferiority margin. As a partner drug with amphotericin B, flucytosine was superior to fluconazole (71 deaths [31.1%] vs. 101 deaths [45.0%]; hazard ratio for death at 10 weeks, 0.62; 95% confidence interval [CI], 0.45 to 0.84; P=0.002). One week of amphotericin B plus flucytosine was associated with the lowest 10-week mortality (24.2%; 95% CI, 16.2 to 32.1). Side effects, such as severe anemia, were more frequent with 2 weeks than with 1 week of amphotericin B or with the oral regimen. CONCLUSIONS: One week of amphotericin B plus flucytosine and 2 weeks of fluconazole plus flucytosine were effective as induction therapy for cryptococcal meningitis in resource-limited settings. (ACTA Current Controlled Trials number, ISRCTN45035509 .).

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.338
Teacher spread0.280 · 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

Citations405
Published2018
Admission routes1
Has abstractyes

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