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Record W4403434825 · doi:10.1186/s13643-024-02662-6

Liver and renal biochemical profiles of people with sickle cell disease in Africa: a systematic review and meta-analysis of case-control studies

2024· review· en· W4403434825 on OpenAlexaff
Silvia Awor, Felix Bongomin, Mark Mohan Kaggwa, Pebalo Francis Pebolo, Jackie Epila, Geoffrey M. Malinga, Christine Oryema, Proscovia Nnamuyomba, Benard Abola, Acaye Ongwech, David Musoke

Bibliographic record

VenueSystematic Reviews · 2024
Typereview
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineMeta-analysisDiseaseIntensive care medicineLiver diseaseSystematic reviewMEDLINEInternal medicineBioinformatics

Abstract

fetched live from OpenAlex

BACKGROUND: Sickle cell disease (SCD) is a genetic blood disorder characterized by a painful vaso-occlusive crisis due to the sickling of red blood cells in capillaries. Complications often lead to liver and renal dysfunctions, contributing to morbidity and mortality, particularly for children under 5. This systematic review and meta-analysis aimed to evaluate the liver and renal functions of people with SCD (HbSS) compared to those without it (HbAA) in Africa. METHODS: The protocol was registered with PROSPERO (CRD42022346771). We searched PubMed, Embase, Web of Science, and Google Scholar using the keywords "liver function", "renal function", "sickle cell disease", and "Africa" on 6th May 2023 for peer-reviewed articles with abstracts in English. We included case-control studies comparing SCD (HbSS) with controls without hemoglobinopathies (HbAA). We used the random-effect model to calculate the pooled average values for the blood tests of people with SCD in RStudio version 4.2.2. RESULTS: = 87%, p < 0.01) compared to controls. CONCLUSION: ) > 50%, there was substantial variation in the reported articles' results. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022346771.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0350.003
Bibliometrics0.0010.002
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 head0.066
GPT teacher head0.329
Teacher spread0.263 · 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 designSystematic review
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

Citations1
Published2024
Admission routes1
Has abstractyes

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