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Record W1702640656 · doi:10.1111/liv.12923

The need for histological subclassification of cirrhosis: a systematic review and meta‐analysis

2015· review· en· W1702640656 on OpenAlexaff
Gaeun Kim, Samuel S. Lee, Soon Koo Baik, Youn Zoo Cho, Moon Young Kim, Sang Ok Kwon, Seung Hwan, Mee-Yon Cho

Bibliographic record

VenueLiver International · 2015
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Calgary
FundersYonsei University
KeywordsCirrhosisMedicineMeta-analysisInternal medicinePortal hypertensionGastroenterologyStage (stratigraphy)Cochrane LibraryPortal venous pressureMEDLINESystematic review

Abstract

fetched live from OpenAlex

BACKGROUND & AIMS: The need for further histological subclassification of cirrhosis has been increasingly recognized because of the heterogeneity of severity within cirrhosis. We sought to identify evidence in the literature regarding the histological subclassification of cirrhosis using the Laennec stage. METHODS: We conducted a systematic review and meta-analysis by searching databases, including MEDLINE, EMBASE and the COCHRANE library, for relevant studies. RESULTS: Of 208 studies identified, 16 were eligible according to the inclusion criteria. With higher grades of the Laennec stage, clinical stages of cirrhosis and Child-Pugh scores/Model for end-stage liver disease scores increased (P < 0.05). Higher Laennec stages were statistically associated with the development of liver-related events, such as liver-related death, liver cancer progression and variceal haemorrhage, as well as higher hepatic venous pressure gradients and higher liver stiffness values (P < 0.05). Two open-labelled studies showed the usefulness of the Laennec system with regard to the evaluation of whether antifibrotic treatments were effective. The mean kappa value was 0.81 (range 0.61-0.87) for inter-observer agreement. CONCLUSIONS: Based on this systematic review and meta-analysis, histological subclassification of cirrhosis using the Laennec system is useful to better predict prognosis and complications of portal hypertension.

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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.724
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.170
GPT teacher head0.392
Teacher spread0.222 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations19
Published2015
Admission routes1
Has abstractyes

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