Bibliographic record
Abstract
Aim:To analyze the frontier and evolution of syndromic surveillance research. Methods:Based on ISI Web of Science and Chinese WanFang data,a detailed TDA bibliometrical analysis on syndromic surveillance research was performed to find out the research contents,institutions,persons and their work,as well research hotspots and future trends about syndromic surveillance research. Results:Based on the foreign documents analysis,the mean citation range per paper was 8. 89,H index was 36,both of them were higher than those of other biomedical fields. After 2001,the publication and cited references increased year by year,the literature was mainly from the main developed countries( such as US,UK,Canada,France and Australia) researchers and research institutions,and they co-operated and the research themes were overlapped. Research hotspot problems included outbreak detection algorithm,syndrome classification and natural language processing,data source and data standardization,visual analytics,evaluation and its application. Syndromic surveillance project in China was still in the initial stage. Conclusion:The finding could provide useful reference and help for our domestic researchers in this field.
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.191 | 0.248 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".