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Tacrolimus without or with the addition of conventional immunosuppressive treatment in juvenile autoimmune hepatitis

2012· article· en· W1575431129 on OpenAlexfundno aff
Joanna Ramirez Marlaka, Nikos Papadogiannakis, Björn Fischler, Thomas Casswall, Eva Beijer, Antal Németh

Bibliographic record

VenueActa Paediatrica · 2012
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsnot available
FundersAstellas PharmaAlberta Innovates - Health Solutions
KeywordsMedicineAzathioprineAutoimmune hepatitisTacrolimusPrednisoloneGastroenterologyInternal medicineAdverse effectHepatitisAbdominal painSurgeryDiseaseTransplantation

Abstract

fetched live from OpenAlex

AIM: To investigate tacrolimus (Tac)-based treatment in juvenile autoimmune hepatitis (AIH). Twenty patients (13 girls; age, 8-17 years; median, 13.25 years) with AIH were treated with two daily oral doses of Tac. Six of them had advanced liver disease and/or cirrhosis. METHODS: Drug concentrations in blood were measured regularly, and the target trough levels were 2.5-5 ng/mL. The patients were followed up for 1 year. Their clinical, biochemical, immunological and histological status was obtained at baseline and after 1 year. RESULTS: In three cases, Tac alone led to complete remission. In 14 cases, additional low doses of prednisolone or azathioprine were used for a short time to achieve remission. In two cases, the treatment was discontinued: in one because of therapeutic failure, in another because of a suspected but unverified adverse event. Ten patients reported headache and/or recurrent abdominal pain. Two patients developed inflammatory bowel disease. Renal function remained intact. CONCLUSION: Tac is a promising alternative first line of treatment for AIH. Although monotherapy with Tac is usually not sufficient to achieve complete remission, the prednisolone and azathioprine doses can be drastically reduced, and most of their side effects avoided.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

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)0.0010.000

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 head0.014
GPT teacher head0.257
Teacher spread0.244 · 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.

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

Citations37
Published2012
Admission routes1
Has abstractyes

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