A study of mechanical ventilation in the ICU after cardiac surgery: a bibliometric analysis
Bibliographic record
Abstract
Background: After cardiac surgery, patients are often admitted to the intensive care unit (ICU) due to various preoperative factors and continue to receive mechanical ventilation. This study sought to conduct a bibliometric analysis to summarize studies on mechanical ventilation among postoperative ICU patients who had undergone cardiac surgery. Methods: We searched the Science Citation Index Expanded (SCI-E) database using the following terms: "cardiac surgery (Topic)", "intensive care (Topic)" and "ventilation (Topic)". The search results were analyzed using R software. The analysis examined the number of publications of relevant articles and the annual change trend, the number of times an article was cited and the annual change trend, the distribution of countries conducting the research, the cooperation between countries and the citation frequency, the distribution of institutions conducting research, the cooperation between institutions, and the citation frequency, the number of published articles, the cooperation among researchers, and the citations frequency of researchers, the journals in which the articles were published, and the use of keywords. Results: A total of 1,969 relevant research papers were included in this study. The main countries that conducted the relevant research included the United States (US), China, Germany, and Canada. The research institutions were mainly located in the US and Canada, and the main researchers were from research institutions in these countries. The most cited authors were Zappitelli, Hichey, and Wypij. According to Bradford's law, 9 core journals in this field were identified. The results of the keyword analysis showed that in the past 10 years, research has focused on the mortality of patients, but only a few related random controlled trials have been conducted. Conclusions: More randomized controlled trials need to be conducted in this field to provide higher evidence-based medical evidence.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.150 | 0.207 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".