Bibliographic record
Abstract
This paper provides the clinician with an understanding of the epidemiologic and biological characteristics of West Nile virus in North America, as well as useful information on the diagnosis, reporting, and management of patients with suspected West Nile virus infection and on advising patients about prevention. Information was gathered from the medical literature and from national surveillance data through May 2002. Since the identification of West Nile virus in New York City in 1999, enzootic activity has been documented in 27 states and the District of Columbia. Continued geographic expansion is likely. Overall, one in 150 infections results in severe neurologic illness. Advanced age is by far the most important risk factor for neurologic disease and, once disease develops, for worse clinical outcome. Surveillance has identified 149 persons with West Nile virus-related illness in 10 states. Encephalitis is more commonly reported than meningitis, and concomitant muscle weakness and flaccid paralysis may provide a clinical clue to the presence of West Nile virus infection. Peak incidence occurs in late summer, although onset has occurred from July through December. Immunoglobulin M antibody testing of serum specimens and cerebrospinal fluid is the most efficient method of diagnosis, although cross-reactions are possible in patients recently vaccinated against or recently infected with related flaviviruses. Testing can be arranged through local, state, or provincial (in Canada) health departments. Prevention rests on elimination of mosquito breeding sites; judicious use of pesticides; and avoidance of mosquito bites, including mosquito repellent use.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".