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Record W2770844446 · doi:10.30699/mmlj17-01-04

An Investigation of the Relationship between Beta-2 Microglobulin (β2M) and Inflammatory Factors (Serum Levels of CRP and Albumin) and High Density Lipoproteins (HDL) in Hemodialysis Patients

2018· article· en· W2770844446 on OpenAlexvenueno aff
Mohammadreza Abdollahzadeh Estakhri, P Kavakeb, Delaram Fathi, Gholamreza Karimi, Amirhosein Mohammadpour, Maryam Rahbar

Bibliographic record

VenueModern Medical Laboratory Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsBeta-2 microglobulinInternal medicineHemodialysisAlbuminMedicineBETA (programming language)Serum albuminC-reactive proteinGastroenterologyEndocrinologyInflammation

Abstract

fetched live from OpenAlex

Background and Objective: Beta-2 Microglobulin (β2M) is a middle molecular weight uremic toxin.Lack of β2M removal leads to β2M accumulation and amyloidosis in hemodialysis patients.The present research aims to determine the relationship between β2M and inflammatory factors, such as C-reactive protein (CRP), albumin, and high-density lipoprotein (HDL) in hemodialysis patients.Methods: Fifty-four hemodialysis patients were selected and their pre-and post-dialysis serum levels of β2M, CRP, albumin, and HDL, were measured.Result: There was an obvious inverse relationship between β2M level and serum level of albumin, while no relationship was found between β2M and CRP or HDL. Conclusion:According to the findings of this study, it was identified that inflammatory-induced increase in β2M level in dialysis patients leads to reduced serum albumin.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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 designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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