Avaliação de células endoteliais circulantes como possíveis marcadores de diagnóstico na pré-eclâmpsia: uma revisão sistemática
Bibliographic record
Abstract
Preeclampsia (PE) is a multifactorial and multisystemic disease, whose pathophysiology involves endothelial injury, increased number of Circulating Endothelial Cells (CECs) and activation of coagulation. It has as diagnostic criteria hypertension, which occurs after 20 weeks of gestation, accompanied by proteinuria and/or target organ dysfunction. PE is associated with several complications both maternal and fetal, which may culminate in death. Thus, the identification of biomarkers with diagnostic power can lead to early interventions, minimizing complications. Some studies have evaluated the association of the number of CECs with the development of PE. Thus, the aim of this study was to investigate whether these cells can be used in the diagnosis of PE. For this, a systematic review was prepared according to PRISMA guidelines and registered in PROSPERO (CRD42021226265). PubMed; Lilacs; Scopus; Embase; Web of Science; Science Direct; Cochrane Library and Gray-literature: CAPES and Google Scholar, were the databases used to search for articles published until July 2022, without language restriction. The clinical question was elaborated according to the PECOT strategy, being eligible observational studies (cross-sectional, case-control and cohort), which evaluated the CECs count in peripheral blood of pregnant women diagnosed with PE and normotensive women, and the main outcome was the difference in CECs count between these patients. The selection by title and abstract, full-text reading, and data extraction steps were performed by two researchers independently and a third for conflict decision. In total, 505 articles were retrieved by the search strategy and 6 articles met the inclusion criteria. The methodological quality of the selected studies was assessed using the Newcastle-Ottawa scale, and all had a low risk of bias. There was a statistically significant increase in the CECs count in most studies when comparing the PE and control groups. Only one study showed that there was no significant difference. Furthermore, the studies also found a positive correlation between increased blood pressure and CECs counts. Thus, the CECs count has great potential to be used as a new diagnostic biomarker for PE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".