Trends in conjunctivochalasis research from 1986 to 2017
Bibliographic record
Abstract
BACKGROUND: With the aging of the population and the use of video terminals, the incidence of Conjunctivochalasis is getting higher, and related research is increasing. So our research aimed to use visualization software to display the research trends of Conjunctivochalasis. METHODS: Retrieved the document (from 1986 to 2017) of conjunctivochalasis in the web of science core collection, analyzed by Citespace V. RESULTS: The main language is English. Article is the key type of document. The average annual number of publications in the time period from 2008 to 2017 was 11.6, which was significantly higher than the period from 1994 to 2007, indicating that the total number of publications has been continuously developed. The law of frequency quoted showed an upward trend yearly. Furthermore, we can find out that Japan, USA, and People's Republic of China were the most productive countries, Kyoto Prefectural University of Medicine was the most prolific institution, Shanghai Jiaotong University is a key institution. The average IF of journals was 3.0508. Cornea and Canadian Journal of Ophthalmology are core journals. Tseng SCG is the most active scholar. All cited author contributed to 5 classifications. Di PMA paper is a classic literature. Huang YK paper can be regarded as the frontier document. All cited-reference dedicated to 7 categories. Conjunctivochalasis is the hot topic, related to observe indicators, risk factors, treatment, graded diagnosis of conjunctivochalasis, etc. In addition, fibroblast was research hotspot. At length, the cluster map of keyword was divided into 7 categories. CONCLUSION: This research will help relevant clinicians and researchers to accurately and quickly grasp the research trends in the field, and continue to conduct new research on the basis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".