Determining the rate of underrecognition of West Nile virus neurologic disease in the province of Quebec in 2012
Bibliographic record
Abstract
BACKGROUND: During a major outbreak of West Nile virus (WNV) infection in the province of Quebec in 2012, public health authorities (PHAs) suspected underrecognition of West Nile neurologic disease (WNND). With data on acute infections detected in blood donors, an estimate of the degree of underrecognition was produced. STUDY DESIGN AND METHODS: All 2012 donors were tested for WNV infection with the use of reverse transcription-polymerase chain reaction (RT-PCR). With the number of cases detected, the number of donors tested, our estimate of the duration of viremia, an estimate of the population at risk, and the ratio of WNND to total cases, an expected number of WNND cases was calculated. A Monte Carlo simulation was used to estimate the range of several of these variables. RESULTS: Seventeen RT-PCR-positive donors were found among 52,309 donations tested. In the base case, the total number of cases was 16,095 and the expected number of WNND cases was 115. In the Monte Carlo simulation, the mean number of expected WNND cases was 136, and the median was 129. Since only 85 cases were reported to PHAs, it is estimated that between 26 and 37.5% of cases occurring in the province went undetected. CONCLUSION: The observation that close to one-third of cases of WNND went undetected because of the omission of appropriate laboratory testing indicates the need for improvement in the investigation of acute neurologic syndrome of suspected infectious etiology in Québec.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".