Progress in epidemiology and prevention strategy research of herpes zoster
Bibliographic record
Abstract
Objective To understand the surveillance status and progress in epidemiological research of herpes zoster in the world and provide evidence for the epidemiological research and the development of feasible surveillance protocol of herpes zoster in Beijing. Methods The analysis was conducted on the surveillance data and epidemiological research literatures of herpes zoster in USA,Canada,Australia and European countries collected through PubMed Database and the domestic literatures related collected through Chinese National Knowledge Infrastructure Database. Result The sentinel surveillance for herpes zoster is currently conducted in USA,Canada,the United Kingdom and France respectively. The surveillance data indicated that the incidence of herpes zoster in age group 65 years increased with year. The incidence in females was higher in males. The risk factors related with herpes zoster included age,gender, mechanical trauma and immunization function. Conclusion The incidence of herpes zoster is closely related with age. The impact is more serious in the elderly. Herpes zoster has not been included in the management for communicable disease in China,it is necessary to strengthen the surveillance for herpes zoster.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".