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Record W2806020880 · doi:10.1186/s12882-018-0915-4

Use of synthetic adrenocorticotropic hormone in patients with IgA nephropathy

2018· article· en· W2806020880 on OpenAlexaff
Bhanu Prasad, Shelley Giebel, Michelle McCarron, Nelson Leung

Bibliographic record

VenueBMC Nephrology · 2018
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsRegina Qu'Appelle Health RegionUniversity of ReginaRegina General Hospital
FundersHealth Research Board
KeywordsMedicineProteinuriaAdrenocorticotropic hormoneNephropathyInternal medicineNephrologyPrednisoneFocal segmental glomerulosclerosisMembranous nephropathyGastroenterologyMinimal change diseaseCyclophosphamideUrologyGlomerulonephritisEndocrinologyHormoneChemotherapyKidneyDiabetes mellitus

Abstract

fetched live from OpenAlex

BACKGROUND: Synthetic adrenocorticotropic hormone (ACTH) has been demonstrated to be effective in patients with membranous nephropathy, minimal change disease and some histological subtypes of focal segmental glomerulosclerosis. Its clinical impact in patients with IgA nephropathy is currently unclear. CASE PRESENTATION: In this report, we describe the clinical use of ACTH in patients with IgA nephropathy. Three female patients (24-44 years) with overt proteinuria received intramuscular (IM) ACTH for varying time periods (8-14 months). Pre-treatment urine protein varied from 2.9 g/d to 4.3 g/d. CONCLUSIONS: There was complete remission in one patient on ACTH monotherapy and in the other two when prescribed as a steroid-sparing agent in combination with cyclophosphamide. All three had resolution in proteinuria to less than 1 g/d and maintained their GFR to baseline values. There were no reported side effects at a once a week dose. This study illustrates that ACTH is an effective agent that is well tolerated with minimal side effects and can be used as an alternative to prednisone in patients with IgA nephropathy.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.234
Teacher spread0.218 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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