Development trend and research hotspots and frontiers of RBC storage lesion based on CiteSpace
Bibliographic record
Abstract
Objective To explore the research status, hotspots, development trend and frontier of RBC storage lesion. Methods The Web of Science core collection database (http: //webofscience.com) was used to retrieve the documents related to " red blood cell storage lesion" from 2005 to 2021. After the exclusion of unrelated documents, CiteSpace (CiteSpace.5.7.R2) was used for bibliometric analysis, including author (all signatories of the article), institution and country (to which the article is affiliated), journal, key words and cited literatures. Results A total of 508 literatures were included, accounting for 91.86% (508/553) of all publication concerning " RBC storage lesion" in this period. The annual growth rate of publications was 14.38%. There were 1 868 authors totally, and 39.76% (202/508) of them published more than 3 papers. D ′Alessandro A from the United States ranked first [7.68% (39/508)], Univ Colorado System and Univ Pittsburgh were the top two institutions [7.28% (37/508) and 7.09% (36/508), respectively]. The United States [53.35% (271/508)], Canada [13.19% (67/508)], the United Kingdom [6.50% (33/508)] and Switzerland [6.10% (31/508)] were the top 4 countries. Keywords co-occurrence network, emergent atlas and literature co-citation cluster atlas mainly focused on mechanism research, clinical trials, improvement of RBC storage conditions and reduction of RBC storage lesion. Conclusion The most important researchers and institutions in the field of RBC storage lesion in the past 17 years were mainly from the United States and Europe. The application of metabolomics and other technologies, the mechanism of RBC storage lesion, the selection of donor diversity, and the research and development of new preservation solutions or additives are the hotspots and frontiers in this 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.061 | 0.073 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".