Severe hand, foot and mouth disease in Shenzhen, South China: what matters most?
Bibliographic record
Abstract
Case report data and a matched case-control study were used to investigate the epidemiological characteristics of hand, foot and mouth disease (HFMD) in children in Shenzhen, China between 2008 and 2011. Multivariate analyses were used to evaluate factors associated with severity of infection. Laboratory tests were performed to determine aetiological identification for samples from 163 severe and fatal cases as well as an outpatient-based HFMD sentinel surveillance system (n = 446). All identified EV71 belonged to sub-genotype C4a. No major changes in the CA16 and EV71 viruses were found until the end of 2011. Annual attack rates and the case-severity ratios (CSRs) rose from 0.82/1000 and 0.56/1000, respectively, in 2008 to 2.12/1000 and 6.13/1000 in 2011. The CSR was higher in migrants than in local residents. The adjusted odds ratio (OR) of having a severe attack for being a migrant was 2.45, having a fever >39°C (OR 5.77), visiting a private clinic (OR 2.65), longer time from symptom onset to diagnosis (OR 1.49), visiting a doctor (OR 1.51), early use of intramuscular pyrazolone (OR 3.36), early use of intravenous glucocorticoids (OR 2.28), or the combination of both (OR 3.75). The mortality and increasing case severity appears to be associated with socioeconomic factors including migration and is of worldwide concern.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".