Acute Encephalitis Syndrome Surveillance, Kushinagar District, Uttar Pradesh, India, 2011–2012
Bibliographic record
Abstract
In India, quality surveillance for acute encephalitis syndrome (AES), including laboratory testing, is necessary for understanding the epidemiology and etiology of AES, planning interventions, and developing policy. We reviewed AES surveillance data for January 2011-June 2012 from Kushinagar District, Uttar Pradesh, India. Data were cleaned, incidence was determined, and demographic characteristics of cases and data quality were analyzed. A total of 812 AES case records were identified, of which 23% had illogical entries. AES incidence was highest among boys<6 years of age, and cases peaked during monsoon season. Records for laboratory results (available for Japanese encephalitis but not AES) and vaccination history were largely incomplete, so inferences about the epidemiology and etiology of AES could not be made. The low-quality AES/Japanese encephalitis surveillance data in this area provide little evidence to support development of prevention and control measures, estimate the effect of interventions, and avoid the waste of public health resources.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".