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Record W2606934987 · doi:10.1177/0961203317702255

Serological biomarkers as risk factors of SLE-associated pulmonary arterial hypertension: a systematic review and meta-analysis

2017· review· en· W2606934987 on OpenAlexaboutno aff
J Wang, Junyan Qian, Y Wang, Jiuliang Zhao, Q Wang, Zhuang Tian, Ming Li, Xiaofeng Zeng

Bibliographic record

VenueLupus · 2017
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOdds ratioConfidence intervalInternal medicinePulmonary hypertensionMeta-analysisCochrane LibraryCardiology

Abstract

fetched live from OpenAlex

Objective This article aims to determine the serological biomarkers which can be considered as risk factors of systemic lupus erythematosus (SLE)-associated pulmonary arterial hypertension by a systematic review and meta-analysis. Methods This study was conducted in accordance with the PRISMA statement. The search database included MEDLINE, EMBASE, Cochrane Library and Scopus. The Newcastle-Ottawa scale was used for the quality assessment. The odds ratio was the primary measure of effect of the risk factors. Results Twelve studies were included in this meta-analysis. The results identified the anti-RNP antibody and anti-Sm antibody as risk factors for SLE-associated pulmonary arterial hypertension with the pooled odds ratios 3.68 (95% confidence interval 2.04-6.63, P < 0.0001) and 1.71 (95% confidence interval 1.06-2.76, P = 0.03), respectively. Conclusion Pulmonary arterial hypertension is a serious complication of SLE with a worse prognosis than SLE patients without pulmonary arterial hypertension. The early recognition of pulmonary arterial hypertension with transthoracic echocardiography routinely performed in SLE patients with risk factors is necessary, especially in Asian 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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
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 score0.418
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0170.005
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.196
GPT teacher head0.386
Teacher spread0.191 · 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.

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

Citations27
Published2017
Admission routes1
Has abstractyes

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