Analysis of the epidemiological characteristics of tuberculosis in Taixing between 2005 and 2010
Bibliographic record
Abstract
Objective To explore the epidemiological characteristics of tuberculosis in Taix-ing city and provide a scientific basis for tuberculosis prevention and control.Methods Registration data of tuberculosis in Taixing from 2005 to 2010 were analyzed.Results A total of 5356 newly-reported tuberculosis cases were registered in Taixing from 2005 to 2010 with the average annual in-cidence rate at 72.99/100 000.Totally,there were 3089 smear positive patients,among which 22.74% were new smear positive patients.The incidence rate was 84.49/100 000 in males and 43.98/ 100000 in females respectively with the sex ratio of 2.46: 1(χ2=874.97,P0.05).Patients were found in different age groups except for those less than 3 years old.Most patients were aged between 55 and 64,accounting for 20.09% of all reported cases.The occupational distribution analysis revealed that farmers,workers and students accounted for a large part,with the proportion at 57.37%,20.03% and 3.58% respectively.Though a little more cases were reported in the sec-ond and third quarter,the seasonal distribution was not significant.Conclusion Tuberculosis pre-vention and control should not be neglected in Taixing.More focuses should be put on students and the elderly people with more comprehensive measures and strategies.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".