Telephone investigation of influenza-like illness and medical care-seeking behavior of Guangzhou residents in pandemic influenza A(H1N1)
Bibliographic record
Abstract
Objective To rapidly assess influenza-like illness(ILI) and common cold morbidity,medical care-seeking behaviors of Guangzhou residents from 1 October to 31 December 2009.Methods Attack rates of ILI and common cold were investigated by using Computer-Assisted-Telephone-Interviewing System(CATI) technology and uniform questionnaire.A descriptive epidemiologic analysis was applied to the survey. Results Altogether 6 325 telephone calls had been made,of which 1 030 were successfully dialed.A total of 505 Guangzhou families(1 197 persons) were investigated,with a successful response rate of 49.03%.The attack rates of ILI and common cold were 6.02% and 8.77%,respectively.The people in the group of junior years,especially 10 years of age,had the highest attack rates(20.99% for ILI and 9.88% for common cold).The attack rate of ILI was 6.19% for male and 5.84 % for female.The attack rate of ILI was 2.92% in November,higher than that(1.84%) in October and that(1.25%)in December(P0.05).Using direct standardization method,ILI incidence among Guangzhou residents during the fourth quarter of 2009 was estimated to be 7.37%(733 194 /9 942 022).The proportions of choosing provincial and municipal hospitals for medical care were the highest both in ILI(41.67%) and common cold(46.67%),respectively.Conclusion The attack rate of ILI in Guangzhou residents in the fourth quarter of 2009 was higher than ever before.People at low age group,particularly children below 10 years old,were subject to seasonal influenza,and should be the key target group for preventing from respiratory infectious diseases.
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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 | 0.001 | 0.000 |
| 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".