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Record W4379259224 · doi:10.1186/s12872-023-03304-w

Meta-analysis of differences in neutrophil to lymphocyte ratio between hypertensive and non-hypertensive individuals

2023· review· en· W4379259224 on OpenAlexaboutno aff
Shirin Sarejloo, Moein Dehesh, Mobina Fathi, Monireh Khanzadeh, Brandon Lucke‐Wold, Arshin Ghaedi, Shokoufeh Khanzadeh

Bibliographic record

VenueBMC Cardiovascular Disorders · 2023
Typereview
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDipperNeutrophil to lymphocyte ratioInternal medicineConfidence intervalLymphocyteAngiologyMeta-analysisOdds ratioBlood pressureAmbulatory blood pressure

Abstract

fetched live from OpenAlex

This study systematically reviewed the evidence regarding differences in the neutrophil to lymphocyte ratio (NLR) level between hypertensive and normotensive individuals as well as between patients with dipper and non-dipper hypertension (HTN). PubMed, Scopus, and Web of Science databases were systematically searched up to 20 December 2021. This was done without any limitation with regard to date, publication, or language. Pooled weighted mean differences (WMD) with 95% confidence intervals (95% CI) were reported. We assessed the quality of studies based on the Newcastle-Ottawa Scale (NOS). In total, 21 studies were included in our study. There was a significant increase in NLR levels for the hypertensive group in comparison to the control group (WMD = 0.40, 95%CI = 0.22-0.57, P < 0.0001). In addition, the NLR levels were higher in the non-dipper than in the dipper group (WMD = 0.58, 95%CI = 0.19-0.97, P = 0.003). Our findings showed that hypertensive patients had higher level of NLR than normotensive individuals.

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)0.008
metaresearch head score (Gemma)0.022
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: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.023
Bibliometrics0.0050.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.153
GPT teacher head0.324
Teacher spread0.171 · 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

Citations28
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBMC Cardiovascular DisordersSame topicInflammatory Biomarkers in Disease PrognosisFrench-language works237,207