MétaCan
Menu
Back to cohort
Record W2030732531 · doi:10.4103/0971-4065.120338

Cyclosporine/ketoconazole reduces treatment costs for nephrotic syndrome

2013· article· en· W2030732531 on OpenAlexaff
Arpana Iyengar, Nivedita Kamath, K Phadke, Martin Bitzan

Bibliographic record

VenueIndian Journal of Nephrology · 2013
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsKetoconazoleMedicineAdverse effectDrugNephrotic syndromeInternal medicinePharmacologyAntifungalDermatology

Abstract

fetched live from OpenAlex

Cyclosporine A (CyA) is an effective agent for the treatment of glucocorticoid-dependent idiopathic nephrotic syndrome (GCDNS), but costs are prohibitive in resource-poor societies. The objectives of this study were to evaluate the efficacy and safety of reducing the dose of CyA by co-administering ketoconazole. A prospective study targeting children 2-18 years of age with GCDNS in remission with CyA monotherapy was conducted. CyA dose was reduced by 50% and ketoconazole was added at 25% of the recommended therapeutic dose, and the drug levels and therapeutic and adverse effects (AE) were monitored. Continued combined therapy after completion of the 4-week trial period was offered. Ten patients (median age 9.5 years, range 3.0-16.0 years) were enrolled in the study. At week 4, the CyA dose was 2.2 ± 0.7 mg/kg/day compared with 5.6 ± 0.9 mg/kg/day at enrolment (P < 0.0001). No AE were noted. All patients continued ketoconazole treatment for at least 3 months. CyA drug cost savings were 61%, and approximately 60% with ketoconazole cost included. The combination of an expensive immunosuppressive drug with a cheap metabolic inhibitor reduced the treatment costs by> 50% without increased adverse events or drug monitoring needs. This intervention demonstrates how access of patients with limited resources to needed drugs can be improved by interference with physiological drug elimination.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.507

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 integrity0.0000.000
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 head0.013
GPT teacher head0.272
Teacher spread0.259 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueIndian Journal of NephrologySame topicRenal Diseases and GlomerulopathiesFrench-language works237,207