Risk factors for pulmonary embolism in ICU patients
Bibliographic record
Abstract
Abstract Background: Pulmonary embolism (PE) is a clinical disease of pulmonary hypertension, right heart failure and pulmonary oxygen uptake dysfunction caused by obstruction of pulmonary artery blood flow caused by embolic substances. The incidence rate of acute PE (ATPE) has been increasing year by year in recent years. And ATPE has become a hot topic of common concern in the departments of severe, emergency, respiratory and heart diseases. Research and clinical practice have proved that early thrombolysis and other specific treatment can greatly improve its prognosis. However, the early diagnosis and treatment rate of ATPE is only 7%, for the lack of simple and objective diagnosis and quantitative evaluation of the degree of ATPE. Therefore, the screening of risk factors for PE in intensive care unit (ICU) patients by systematic review and meta-analysis is an important preliminary work to carry out the early diagnosis model of PE. Methods: We will retrieve eight electronic databases from their inception to May 31, 2021, which include PubMed, Cochrane Library, Excerpt Medical Database (Embase), Web of science, China Biology Medicine disc, VIP database (VIP), Wan Fang database, China National Knowledge Infrastructure. Methodological quality of all studies was assessed using the Newcastle-Ottawa scale. We will use STATA V.13.0 software and RevMan5.3 for data analysis, and risk factor effect sizes will be expressed as an odds ratio and their 95% Cl. Results: We will disseminate the findings of this systematic review and meta-analysis via publications in peer-reviewed journals. With this study, we can get the significant risk factors and risk ratio of PE in ICU patients. Conclusion: This study systematically reviewed the existing evidence and determined the risk factors for PE in ICU patients. This study provides a basis for the establishment of early warning and prediction model of PE in ICU 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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".