INVESTIGATION OF THE GLOBAL OUTCOMES OF ACUTE RESPIRATORY DISTRESS SYNDROME WITH THE EFFECT OF COVID-19 IN PUBLICATIONS: A BIBLIOMETRIC ANALYSIS BETWEEN 1980 AND 2020
Bibliographic record
Abstract
Objective: Acute respiratory distress syndrome (ARDS) is regarded as a serious complication with high mortality rates and constitutes an important health problem during the COVID-19 pandemic. Therefore, a thorough bibliometric study on ARDS is needed. In this study, it was aimed to holistically summarize the articles published on ARDS between the years 1980 and 2020 using statistical methods and bibliometric analyses.Material and Methods: The literature was scanned using the Web of Science (WoS) database. Keywords used on WoS included “acute respiratory distress syndrome”, “adult respiratory distress syndrome” and “ARDS”. The search was carried out on the “titles” of the publications, and the articles obtained were bibliometrically analyzed. Linear and non-linear regression analysis was used in order to estimate the number of future studies.Results: A total of 11.934 publications were found. Of these publications, 5402 were articles (45.3%) on which the bibliometric analysis was performed. A high increase trend was observed in the number of publications during COVID-19. Most articles were published in the field of Critical Care Medicine (1965, 36.4%). The top four countries contributing to the literature were the USA (1967, 36.4%), Germany (534, 9.9%), France (534, 9.9%), and China (534, 9.9%). The most active 4 institutions were confirmed as theUniversity of Toronto (154), University of California San Francisco (153), University of Washington (153) and University of Harvard (151). The first 2 journals with the most publications were Critical Care Medicine (394) and Intensive Care Medicine (248).Conclusion: In this comprehensive bibliometric study on ARDS on which the number of research increases day by day with the effect of the COVID-19 pandemic, a summarized information of 5402 articles published between 1980 and 2020 was reported. This study will be a guide for scientists and clinicians regarding the global output of ARDS.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.136 | 0.161 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".