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Record W4283745280 · doi:10.1093/labmed/lmac059

Chemerin Levels in Acute Coronary Syndrome: Systematic Review and Meta-Analysis

2022· review· en· W4283745280 on OpenAlexaboutno aff
Abdulrahman Ismaiel, Mohammad Zeeshan Ashfaq, Daniel‐Corneliu Leucuta, Mohamed Ismaiel, Dilara Ensar Ismaiel, Stefan‐Lucian Popa, Dan L. Dumitraşcu

Bibliographic record

VenueLaboratory Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsChemerinMedicineAcute coronary syndromeMeta-analysisInternal medicineCochrane LibraryDiabetes mellitusAnginaMyocardial infarctionEndocrinologyInsulin resistanceObesityAdipokine

Abstract

fetched live from OpenAlex

OBJECTIVE: We evaluated the relevant published studies exploring the association between chemerin concentrations and acute coronary syndromes (ACSs). METHODS: A systematic search was performed in October 2021 using PubMed, Scopus, Embase, and Cochrane Library. We included full articles and assessed their quality using the Newcastle-Ottawa score. RESULTS: We found 6 studies in the systematic review and 5 of these were included in our meta-analysis. Mean difference (MD) of 41.69 ng/mL (95% CI, 10.07-73.30), 132.14 ng/mL (95% CI, -102.12-366.40), and 62.10 ng/mL (95% CI, 10.31-113.89) in chemerin levels was seen in ACS patients vs control subjects, ACS patients vs stable angina pectoris patients (SAP), and type 2 diabetes mellitus (T2DM) ACS patients vs nondiabetic ACS patients, respectively. CONCLUSION: Chemerin levels were significantly elevated in patients with ACS compared to controls, as well as in T2DM-ACS patients compared to nondiabetic ACS patients. However, no significant MD in chemerin levels was observed between SAP and ACS patients.

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)0.009
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.023
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0170.023
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.085
GPT teacher head0.361
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2022
Admission routes1
Has abstractyes

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