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Record W4362578526 · doi:10.4102/sajid.v38i1.470

Prescribing patterns of echinocandins in adult patients in a private hospital in Gauteng, South Africa

2023· article· en· W4362578526 on OpenAlexaff
Anja Grey, Rianda Joubert, Stephan F. Steyn, Marlene Julyan

Bibliographic record

VenueSouthern African Journal of Infectious Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsAnidulafunginCaspofunginEchinocandinsMedicineMicafunginObservational studyAntifungalInternal medicineAntifungal drugIntensive care medicineFluconazoleDermatology

Abstract

fetched live from OpenAlex

Background: Worldwide, the leading cause of invasive candidiasis and the fourth leading cause of hospital-acquired infections are the Candida species (spp.) group. One of the most important tools in fighting such drug-resistant fungi is the appropriate use of antifungal agents. Objectives: The study aimed to determine echinocandins' general prescribing patterns and how they are associated with the treatment period. Method: A quantitative, observational, and descriptive was used, and included patients receiving antifungal treatment in a private hospital in Gauteng, South Africa between 01 January 2015 to 31 December 2015. Results: Of the 146 patient files included, 102 patients (69.9%) received caspofungin and 44 patients (30.1%) were treated with anidulafungin. For the former, 99 (97.1%) patients received a loading dose (LD) of 70 mg, while 200 mg anidulafungin was only prescribed to 30 patients (68.2%). In line with maintenance dose guidelines, the majority (98.1%) of caspofungin-treated patients received 50 mg IV daily, whereas 4 (3.9%) patients were treated at higher doses (70 mg daily). Anidulafungin was administered at various maintenance doses, including 400 mg (2.3% of patients), 200 mg (52.3%), 100 mg (43.2%) and 50 mg (2.3%) IV daily. Conclusion: Our results can be utilised to produce a hospital-specific algorithm in terms of Candida-infected patients. Contribution: spp. Infections.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.610

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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