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Record W4200371457 · doi:10.1155/2021/4431907

Was Antiphospholipid Syndrome a Risk Factor of Stroke? A Systemic Review and Meta-Analysis of Cohort Studies

2021· review· en· W4200371457 on OpenAlexaboutno aff
Kai Zhao, Ping Zhou, Ling Xu, Ruili Li, Jincai Yang, Zhang Qiang, Mingfei Yang, Xiaoxing Wei

Bibliographic record

VenueDisease Markers · 2021
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePublication biasMeta-analysisHazard ratioAntiphospholipid syndromeStroke (engine)Internal medicineConfidence intervalCohortFunnel plotCohort studyRisk factorRelative riskThrombosis

Abstract

fetched live from OpenAlex

Antiphospholipid syndrome (APS) is characterized by thrombosis. This systemic review and meta-analysis was to verify the hypothesis that APS might increase the risk of stroke. Studies were identified after literature searching of PubMed, Embase, and Cochrane. Newcastle-Ottawa Quality Assessment Scale Cohort Studies (NOQAS-C) was used to assess the quality of studies. The pooled effect with 95% confidence interval (95% CI) was calculated by random-effect model. I -square ( I 2 ) was used to test heterogeneity. Funnel plot was used to evaluate publication bias. A total of 17 cohort studies with overall high quality were included. There was no publication bias. Pooled hazard ratio of stroke occurrence in APS patients was 1.76 (1.39-2.21) with low heterogenicity and stable result from sensitivity analysis. In the analysis of subgroups, pooled risk ratios of stroke occurrence in patients with only positive antibodies of APS diagnosis were 1.75 (0.99-3.09), which for the APS patients with other autoimmune diseases were 14.70 (7.56-28.56). APS might be a risk factor of stroke, especially in patients with other autoimmune diseases.

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)0.020
metaresearch head score (Gemma)0.040
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 score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.040
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.043
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.108
GPT teacher head0.401
Teacher spread0.293 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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