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Record W4318754253 · doi:10.1002/hsr2.1080

<i>Retracted</i>: Gut microbiota and COVID‐19: A systematic review

2023· review· en· W4318754253 on OpenAlexaboutno aff
SeyedAhmad SeyedAlinaghi, Arian Afzalian, Zahra Pashaei, Sanaz Varshochi, Amirali Karimi, Hengameh Mojdeganlou, Paniz Mojdeganlou, Armin Razi, Farzaneh Ghanadinezhad, Alireza Shojaei, Ava Amiri, Mohsen Dashti, Afsaneh Ghasemzadeh, Omid Dadras, Esmaeil Mehraeen, Amir Masoud Afsahi

Post-publication record

NatureRetraction
ReasonCompromised Peer Review;Unreliable Results and/or Conclusions;
Date7/25/2023 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueHealth Science Reports · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
FundersKhalkhal University of Medical SciencesTehran University of Medical Sciences and Health Services
KeywordsGut floraPopulationImmunologyDiseaseMedicineImmune systemCoronavirusCoronavirus disease 2019 (COVID-19)BiologyInternal medicineInfectious disease (medical specialty)Environmental health

Abstract

fetched live from OpenAlex

Background and Aims: Alteration in humans' gut microbiota was reported in patients infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The gut and upper respiratory tract (URT) microbiota harbor a dynamic and complex population of microorganisms and have strong interaction with host immune system homeostasis. However, our knowledge about microbiota and its association with SARS-CoV-2 is still limited. We aimed to systematically review the effects of gut microbiota on the SARS-CoV-2 infection and its severity and the impact that SARS-CoV-2 could have on the gut microbiota. Methods: We searched the keywords in the online databases of Web of Science, Scopus, PubMed, and Cochrane on December 31, 2021. After duplicate removal, we performed the screening process in two stages; title/abstract and then full-text screening. The data of the eligible studies were extracted into a pre-designed word table. This study adhered to the PRISMA checklist and Newcastle-Ottawa Scale Bias Assessment tool. Results: Sixty-three publications were included in this review. Our study shows that among COVID-19 patients, particularly moderate to severe cases, the gut and lung microbiota was different compared to healthy individuals. In addition, the severity, and viral load of COVID-19 disease would probably also be influenced by the gut, and lung microbiota's composition. Conclusion: Our study concludes that there was a significant difference in the composition of the URT, and gut microbiota in COVID-19 patients compared to the general healthy individuals, with an increase in opportunistic pathogens. Further, research is needed to investigate the probable bidirectional association of COVID-19 and human microbiome.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.134
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.134
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0150.018
Science and technology studies0.0020.002
Scholarly communication0.0060.009
Open science0.0050.005
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0220.002

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 head0.076
GPT teacher head0.437
Teacher spread0.361 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2023
Admission routes1
Has abstractyes

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