A Bibliometric Analysis and Mapping of On-Line Registration System in Hospital
Bibliographic record
Abstract
This study analyzes the Bibliometric study about online registration at the hospital. This analysis includes statistical information obtained from the Scopus database of 1,456 research journals taken from 1999 to 2019. Keywords verified from the survey result are used to retrieve relevant articles from the database. The result of the study shows that the journal article occupies the top position is "Gefitinib plus the best supportive care in patients previously treated with difficult to cure non-small lung cancer: Results of a multicentre, multicentre randomized, placebo-controlled study (Evaluation of Iressa Survival in Lung Cancer" written by Thatcher N., Chang A ., Parikh P., JR Pereira, Ciuleanu T., Von Pawel J., Thongprasert S., Tan EH, Pemberton K., Archer V., Carroll K with the number of citation 1,852 in 2005. The best author who wrote a journal article related to online registration is Jaffray, DA which donated nine research article publications related to online registration. The institution that most donated article publications is the University of Toronto, 54 journal articles. The majority of paper publications was dominated by United State with 326 papers. The number of articles written with this theme have increased from year to year, in other words this theme is still a tranding topic to be researched and developed by researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.131 | 0.208 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".