The application of procalcitonin in respiratory diseases: an evaluation of the current research literature
Bibliographic record
Abstract
BACKGROUND: Procalcitonin (PCT) can effectively identify bacterial infections and can be used for risk prediction and antimicrobial treatment in patients with unexplained fever and critically ill patients. In this study, statistical analyses of the literature were performed to clarify the application and research status of PCT in respiratory diseases. Future research directions are discussed. METHODS: A literature search was conducted using the Science Citation Index Expanded (SCI-EXPANDED) database in the Web of Science Core Collection (WOSCC). Published literature between 1995 and February 6, 2021 were searched using the following strategies: subject term = procalcitonin; and Web of Science categories = Respiratory System. Using the Citespace software, the literature on the application of PCT in patients with respiratory diseases was analyzed in terms of annual publication status, subject distribution, country/institution distribution, journal distribution, author distribution, and keywords. RESULTS: A total of 542 related research literatures were identified, with the number of published papers and the number of literature citations increasing yearly. Research was mainly concentrated in the United States, China, Switzerland, and other countries, with countries such as the United Kingdom, the United States, and Canada being involved in international collaborations. Research institutions were mainly universities or hospitals such as the University Hospital of Basel, University of Barcelona, and Northwestern University. In particular, the University Hospital of Basel had extensive inter-hospital collaborations. Stolz et al. published many related papers, but the centrality value was low. Authors including Christ-Crain M, Schuetz P, and Stolz D were highly cited. Journals such as the American Journal of Respiratory and Critical Care Medicine, Chest, and the European Respiratory Journal were more influential. Keyword analysis showed that sepsis and pneumonia are the current hot topics. CONCLUSIONS: Related papers mainly focused on respiratory infections, especially sepsis and pneumonia. There were also a small number of studies suggesting that PCT is related to tumors.
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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 | 0.029 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.100 | 0.069 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".