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Record W4296476218 · doi:10.1136/gutjnl-2022-basl.117

P66 Risk factors for post-transplant diabetes mellitus in liver transplant recipients: A Systematic review and Meta-analysis

2022· review· en· W4296476218 on OpenAlex
Muhammad Ali Tariq, Hamza Amin, Bilal Ahmed

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAbstracts · 2022
Typereview
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLiver transplantationInternal medicineMeta-analysisDiabetes mellitusOdds ratioBody mass indexType 2 Diabetes MellitusTransplantationEndocrinology

Abstract

fetched live from OpenAlex

<h3>Background</h3> Post-transplant diabetes mellitus (PTDM) is one of the most common metabolic complications following liver transplant (LT) that reduces graft and recipient overall survival. An accurate and detailed understanding of PTDM related risk factors after LT is critical for improving patient outcomes and preventing PTDM incidences. Therefore, we aimed to conduct a meta-analysis to identify risk factors for the development of PTDM following a liver transplant. <h3>Methods</h3> Two reviewers independently searched three electronic databases Cochrane Central, Scopus, and PubMed until May 2022 for case-control studies that investigated risk factors of PTDM diagnosed in adult liver transplant recipients. The methodological quality of the included studies was assessed with Newcastle-Ottawa Scale (NOS) tool. Meta-analysis was performed using random-effects models to calculate odds ratios (OR) for dichotomous outcomes and weighted mean differences (WMD) for continuous data. A p-value &lt;0.05 was considered to be statistically significant. Statistical analyses were conducted using Stata version 17.0. <h3>Results</h3> 31 studies with 20,536 participants including 5131 with PTDM after liver transplantation, were included in the meta-analysis. Our pooled analysis shows that development of PTDM after LT is significantly associated with recipient age (WMD: 3.63, 95% CI [2.20, 5.07]; p &lt; 0.001), recipient body mass index (WMD: 1.37, 95% CI [0.91, 1.83]; p &lt; 0.001), male gender (OR: 1.55, 95% CI [1.35, 1.79]; p &lt; 0.001), family history of diabetes mellitus (OR: 2.04, 95% CI [1.46, 2.63]; p =0.02), impaired fasting glucose (OR: 1.66, 95% CI [1.24, 2.08]; p =0.02), hepatitis C infection (OR: 2.05, 95% CI [1.80, 2.30]; p &lt;0.001), alcoholic liver disease (OR: 1.31, 95% CI [1.05, 1.57]; p =0.04), cytomegalovirus infection (OR: 2.80, 95% CI [ 2.29, 3.31]; p &lt;0.001), history of hypertension (OR: 2.11, 95% CI [1.41, 2.80]; p =0.04) and acute rejection (OR: 1.44, 95% CI [1.23, 1.64]; p &lt;0.001). However, hepatitis B infection and immunosuppressants such as tacrolimus, cyclosporine, sirolimus, and steroids were not associated with PTDM (p&gt;0.05). <h3>Conclusions</h3> This study has identified multiple risk factors such as older age, male gender, high BMI, pre-transplant impaired fasting glucose, family history of diabetes, history of hypertension, alcoholic liver disease, cytomegalovirus and hepatitis C infection to be independently associated with an increased risk of PTDM after liver transplant. Some of the identified factors are potentially modifiable. Therefore, large epidemiological studies are still needed for further investigation.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0100.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.110
GPT teacher head0.354
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