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Record W4382753312 · doi:10.36216/jpd.v7i1.206

Sequential organ failure assessment (SOFA) score as a predictor of acute kidney injury in COVID-19 patients: a systematic review and meta-analysis

2023· review· en· W4382753312 on OpenAlexaboutno aff
Nicolas Daniel Widjanarko, Erich Tamio, Steven Alvianto, Nadhea Debrinita Surya, Rexel Kuatama, Maria Riastuti Iryaningrum

Bibliographic record

VenueJurnal Penyakit Dalam Udayana · 2023
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
FundersFaculty of Medicine and Health, University of SydneyUniversitas Katolik Indonesia Atma JayaUniversitas Indonesia
KeywordsMedicineSOFA scoreAcute kidney injuryInternal medicineCoronavirus disease 2019 (COVID-19)Meta-analysisIntensive care unitDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background: The sequential organ failure assessment (SOFA) score is a tool used to describe organ failure in critically ill patients. Studies have showed that coronavirus disease 2019 (COVID-19) patients who develop predictor of acute kidney injury (AKI) often have an increased SOFA. Objective: This study aimed to evaluate the potential of SOFA score as a predictor AKI in COVID-19 patients. Methods: A systematic search was conducted on PubMed, Google Scholar, EBSCO-Host, and ProQuest. The risk of bias was assessed using the Newcastle-Ottawa Scale . Result: Out of the 9 studies reviewed, 7 showed a significant association between SOFA score and AKI. The meta-analysis of 3 studies gave mean differences of 1.66 in favor of the AKI group (95% CI 1.12 - 2.21). The heterogeneity was low (Tau2 result = 0.06, I2 result = 22% with p = 0.28) and the significant results for the overall effect showed a value of p < 0.00001. Conclusion: The SOFA score has the potential to be a good predictor of AKI development in COVID-19 patients, with a significant mean difference.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0200.005
Bibliometrics0.0030.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.106
GPT teacher head0.446
Teacher spread0.339 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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