No evidence of over‐reporting of SARS in mainland China
Bibliographic record
Abstract
OBJECTIVE: To find out whether there was over-reporting of SARS patients in mainland China in view of the relatively low case fatality ratio in mainland China, in comparison with other affected countries and areas. METHODS: We searched PubMed for all SARS antibody detection papers (in English or Chinese language) using the keywords 'SARS' and 'antibody'. Then the resulting articles were further read through to select the SARS detection results using ELISA methods of serum samples collected at least 1 month after disease onset. A multi-level logistic regression was applied to test for possible differences in the proportions positive between locations of the study. RESULTS: A total of 48 studies were identified, including 39 from mainland China and nine from elsewhere (Hong Kong, Taiwan, Canada and Vietnam). For mainland China, there was no difference between Guangdong, Beijing and other provinces in the proportions testing positive (83.0%, 85.8% and 85.4% respectively). The grand average of 84.2% seropositive was lower than the 95.1% for the countries and areas outside of mainland China combined. However, this difference was far from significant after correcting for dependency of individual tests within the same study. CONCLUSIONS: Our study showed no evidence of over-reporting of SARS in mainland China, nor in Guangdong, where the SARS epidemic started. Even if the lower seroprevalence in mainland China, relative to other affected areas, does represent actual over-reporting, then this factor can only explain a modest 10% of the lower case fatality in mainland China.
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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".