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Record W4410384682 · doi:10.1016/j.metop.2025.100370

Recovery time of diabetic ketoacidosis in Africa: Systematic review and meta-analysis

2025· article· en· W4410384682 on OpenAlexaboutno aff
Tadios Lidetu, Simon Birhanu, Addisu Simachew Asgai, Tsegaamlak Kumelachew Derse, Yideg Abinew, Moges Tadesse, Desalegn Mitiku, Jemberu Chane, Banchamilak Adane, Tadele Kassahun Wudu, Betelhem Mekonnen, D Tamiru

Bibliographic record

VenueMetabolism Open · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetic ketoacidosisMeta-analysisMedicineDiabetes mellitusIntensive care medicineKetoacidosisInternal medicineEndocrinologyType 1 diabetes

Abstract

fetched live from OpenAlex

ABSTRACT Background Diabetes mellitus is a long-term metabolic disease marked by consistently elevated blood glucose levels. Diabetic ketoacidosis is the medical consequence of diabetes mellitus that has the highest attributed fatality rate. Socioeconomically differences affect how long it takes to recover from diabetic ketoacidosis. A few research were carried out in Africa to demonstrate how long diabetic ketoacidosis takes to recover. However, the pooled median recovery time and predictors of diabetic ketoacidosis have not been studied in Africa. Thus, determine the pooled median recovery time and predictors of diabetic ketoacidosis in Africa was the aim of this systematic review and meta-analysis. Methods To find available publications, a number of databases were analyzed, including PubMed, Science Direct, Cochrane, Hinari, Google Scholar, grey literature, and articles from various university repository sites. Microsoft Excel version 13 was used to extract and sort the data before exporting it to STATA/MP 17.0 for analysis. The quality of each study was evaluated using the Newcastle-Ottawa Scale. A 95 percent confidence interval Der Simonian random-effects model was employed to investigate the pooled recovery time of diabetic ketoacidosis. Publication bias and heterogeneity were assessed using the Egger's test and I 2 . Both meta-regression and subgroup analysis were used to determine the potential source of heterogeneity. Statistical significance was defined as P-values below 0.05. Result The pooled median recovery time for diabetic ketoacidosis in Africa was 38 hours (95 percent CI: 33–43 hours), according to this comprehensive review and meta-analysis. Significant heterogeneity is evident when looking at the Galbraith plot with I2 = 100% (p < 0.001). Research conducted after 2020 revealed that diabetic ketoacidosis has a long recovery time of 40 hours (95 percent CI: 3-77 hours). However, research with fewer than 300 participants showed that diabetic ketoacidosis recovered more quickly: 18 hours (95 percent confidence interval: 12–24 hours). Conclusion Among patients with diabetic ketoacidosis in Africa, the pooled median recovery time was lengthy. The recovery time from diabetic ketoacidosis was influenced by a number of factors, including the severity of the diabetic ketoacidosis, the delay in starting therapy, and the length of time the patient had diabetes mellitus, and elevated blood glucose levels. Diabetic ketoacidosis recovery time can be shortened by altering these factors.

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)0.001
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: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.259
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

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
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
Published2025
Admission routes1
Has abstractyes

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