Bibliometric Analysis of The Last 40 Years of Chest Journal
Bibliographic record
Abstract
Chest Journal, which began to be scanned in the Web of Science in 1980, is one of the leading journals in the field of Critical Care Medicine and Respiratory System (quartile 1). In this study, the research trends of the publications in the Chest Journal were examined using three different bibliometric analysis programs (Bibliometrix, VOSviewer, and CiteSpace) in the period between 1980-2019. Along with the main statistics, keyword co-occurrence network map, density document co-citation, time map, and burst (references) analysis were performed. According to the results of the analysis, the research trends of Chest Journal were subject to discussion. The countries with the most publications are America, Canada, the United Kingdom, France, and Japan. According to the Co-Occurrence network map analysis, Chest journal's publications consist of clusters of cellular structures, thoracic oncology, chest infection, pulmonary and cardiovascular, sleep and pulmonary function test, and obstructive lung diseases. The studies in the "COPD / Formoterol Metered Dose inhaler" and "Patients / VTE disease chest guideline" clusters were found to be the most recent studies. This article has the potential to provide a valuable reference for scientists to understand Chest Journal's research trends and to grasp current issues in the field.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.083 | 0.113 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".