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Record W2395283519

Association between angiotensinogen M235T polymorphism and hypertrophic cardiomyopathy.

2015· article· en· W2395283519 on OpenAlexaboutno aff
Si-Jia Sun, Yafeng Zhou, Lang-Biao Xu, Xiangjun Yang, Xiaodong Qian

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldMedicine
TopicRenin-Angiotensin System Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHypertrophic cardiomyopathyMedicineInternal medicineMeta-analysisAlleleCochrane LibraryGastroenterologyGeneticsGeneBiology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: To investigate the relationship between angiotensinogen (AGT) gene M235T polymorphism and hypertrophic cardiomyopathy (HCM) to explore the potential role of the AGT polymorphism in HCM. METHODS: PubMed, Embase, OVID, Cochrane library, CNKI, Wan Fang Database were searched to identify the studies involving AGT M235T polymorphism and HCM. Two authors performed independent literature review and study quality assessment using the Newcastle-Ottawa Scale (NOS) checklist. A random-effects model was used to calculate the overall combined risk estimates. RESULTS: Nine studies involving 887 cases and 1407 controls were included in our meta-analysis. No significant associations were found between AGT M235T polymorphism and HCM (allele model T vs M: OR = 1.17, 95% CI = 0.95-1.45; dominant model TT vs (MM/MT): OR = 1.21, 95% CI = 1.00-1.45; recessive model (TT/MT) vs MM: OR = 1.12, 95% CI = 0.87-1.45; heterozygous comparison MT vs MM: OR = 1.07, 95% CI = 0.82-1.41; homozygous comparison TT vs MM OR = 1.19, 95% CI = 0.88-1.61. In subgroup analysis, the significant difference of association between AGT M235T polymorphism and HCM existed in Asian and sporadic hypertrophic cardiomyopathy (SHCM), but no significant difference was found in Europeans and familial hypertrophic cardiomyopathy (FHCM). CONCLUSIONS: There is no association between AGT M235T polymorphism and HCM in general populations, but such a relationship exists in Asians and SHCM.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.021
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.233
Teacher spread0.184 · 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 teacher head, 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

Citations6
Published2015
Admission routes1
Has abstractyes

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