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Record W4316015345 · doi:10.3138/canlivj-2022-0031

Impact of decompensated cirrhosis in children: A population-based study

2023· article· en· W4316015345 on OpenAlexafffundvenueabout
Mohit Kehar, Rebecca Griffiths, Jennifer A. Flemming

Bibliographic record

VenueCanadian Liver Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsQueen's UniversityChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersOntario Ministry of Health and Long-Term Care
KeywordsDecompensationMedicineCirrhosisLiver transplantationLiver diseasePopulationPediatricsTransplantationAscitesIncidence (geometry)Internal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Background: We describe the proportion of children with compensated cirrhosis who develop decompensation in Ontario, Canada over the past two decades. Methods: This is a retrospective population-based cohort study using routinely collected health care data from Ontario, Canada held at ICES during 1997-2017. Diagnosis of cirrhosis was made using validated ICES definition, and decompensation events were defined according to validated coding. Rates of decompensation, type of decompensation, and incidence of liver transplantation after decompensation were analyzed. Databases were linked at the individual level and analyzed at ICES-Queen's. Results: = 0.03). Ascites (137/253, 54%) was the most frequent complication. 199/2755 (7%) of children with cirrhosis received liver transplantation, of which 64% (128/199) occurred after a decompensation event. Overall, a total of 132 (4.7%) deaths occurred during the study period, with 55 deaths following a decompensating event. Conclusion: We present the first study to describe rates of decompensation, type, and rate of liver transplantation after decompensation in pediatric cirrhosis at the population level. To improve the care of children with liver disease, early detection of liver disease, early initiation of specific treatments as well as identification of children who are at risk of becoming decompensated are crucial.

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 score0.114
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.286
Teacher spread0.271 · 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

Citations0
Published2023
Admission routes4
Has abstractyes

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