Comparison between the clinical and laboratory features of enterovirus and West Nile virus infections
Bibliographic record
Abstract
The seasonality and clinical features of enterovirus (EV) infections overlap with those of West Nile virus (WNV). The purpose of this study was to determine the frequency of EV detection in patients being tested for WNV and to look for features that could be used to distinguish between infections with these two viruses. Nucleic acid amplification testing (NAT) for EV was performed on all plasma samples submitted for WNV testing in 2003 and 2004. Demographics, clinical features, and laboratory results for patients with documented EV viremia were compared with those for patients with confirmed WNV infection (as diagnosed by NAT and/or serology). NAT for EV was positive on 50 of 1,784 serum or plasma samples submitted for WNV testing (2.8%). Clinical information was compared for 45 patients with EV viremia and 214 patients with WNV infection. Patients with EV viremia were younger and less likely to have heart disease or a travel history (P<0.05). The EV viremia cases were distributed throughout the whole province while the WNV cases were predominantly in the southern part of the province. Symptoms were remarkably similar, although patients with WNV infection were more likely to have anorexia, dizziness, rash, and cranial nerve palsy (P<0.05). There are no consistent differences in the features of WNV infection and enteroviral viremia so diagnostic tests for both viruses should be performed when WNV is present in local mosquitoes.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".