Bibliographic record
Abstract
Background: On April 14, 2017, two measles cases were reported by local health practitioner from Farash Town. A team of FELTP fellows were deputed to investigate the outbreak. Objective: On the request of district health authorities an outbreak investigation was conducted to assess magnitude, identify risk factors and recommend control measures. Methods: Outbreak investigation was carried out from April 18 to May 05, 2017. Active case finding was conducted through a house-to-house survey. A case was defined as onset of maculopapular rash with fever and presence of any of the sign/symptoms like coryza, conjunctivitis and cough in a resident of Farash Town from March 25, 2017 to April 30, 2017. Community-based age and sex-matched controls were selected. Vaccine coverage survey was conducted in a cluster of 245 houses. Frequencies were calculated, attack rates computed, and vaccine efficacy was determined. Blood samples of 03 suspected cases were sent to Public Health Laboratories at NIH Islamabad for confirmation. Results: A total of 15 cases were identified; 13 through active case finding. Mean age was 44.5 months (range 05-120 months). The cases were predominantly male n=9(60%). Overall attack rate was 1.15% and most severely affected age group was 24-36 months (n=4, AR=10.81%) followed by 12-24 months (n=3, AR=8.10%). Diarrhea developed in n=12 (80%) and pneumonia developed in n=2 (13.3%). Immunization coverage survey showed that AR in unvaccinated was 25% and AR in vaccinated was 10%, hence the vaccine efficacy was calculated to be 60%. The most significant reason for non-vaccination was misconception about vaccination (OR: 24.0, CI: 4.9-116.1). All blood samples were positive for measles-specific IgM on ELISA. Conclusions: Low immunization status was the most probable cause of outbreak. The results were communicated to district health authorities for mass vaccination. Health awareness session was conducted for all households. No new case was reported during the two weeks follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".