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Record W2426505168 · doi:10.1016/j.cgh.2016.06.008

HBV-Associated Acute Liver Failure After Immunosuppression and Risk of Death

2016· article· en· W2426505168 on OpenAlexafffund
Constantine Karvellas, Filipe S. Cardoso, Michelle Gottfried, K. Rajender Reddy, A. James Hanje, Daniel Ganger, William M. Lee, William M. Lee, Anne M. Larson, Iris Liou, Oren K. Fix, Michael L. Schilsky, Timothy M. McCashland, J. Eileen Hay, Natalie Murray, A. Obaid S. Shaikh, Andrés T. Blei, Daniel Ganger, Atif Zaman, Steven Han, Robert J. Fontana, Brendan M. McGuire, Raymond Chung, Alastair D. Smith, Robert S. Brown, Jeffrey S. Crippin, Edwin Harrison, Adrian Reuben, Santiago J. Muñoz, R. Todd Stravitz, Lorenzo Rossaro, Raj Satyanarayana, Tarek Hassanein, Jodi Olson, Ram Subramanian, James Hanje

Bibliographic record

VenueClinical Gastroenterology and Hepatology · 2016
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsUniversity of Alberta
FundersBaylor University Medical CenterUniversity of AlbertaUniversity of California, San DiegoUniversity of OregonUniversity of PittsburghUniversity of California, Los AngelesUniversity of South CarolinaUniversity of Texas Southwestern Medical CenterNational Institute of Diabetes and Digestive and Kidney DiseasesBaylor UniversityNorthwestern UniversityMassachusetts General HospitalMedical University of South CarolinaNational Institutes of HealthOhio State UniversityKing's College LondonVirginia Commonwealth UniversityYale UniversityUniversity of WashingtonEmory UniversityUniversity of Pennsylvania
KeywordsMedicineImmunosuppressionLiver failureLiver transplantationIntensive care medicineInternal medicineVirologyTransplantation

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.310
Teacher spread0.289 · 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

Citations40
Published2016
Admission routes2
Has abstractno

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