Poliomyelitis surveillance: the model used in India for polio eradication.
Bibliographic record
Abstract
Poliomyelitis surveillance in India previously involved the passive reporting of clinically suspected cases. The capacity for detecting the disease was limited because there was no surveillance of acute flaccid paralysis (AFP). In October 1997, 59 specially trained Surveillance Medical Officers were deployed throughout the country to establish active AFP surveillance; 11,533 units were created to report weekly on the occurrence of AFP cases at the district, state and national levels; timely case investigation and the collection of stool specimens from AFP cases was undertaken; linkages were made to support the polio laboratory network; and extensive training of government counterparts of the Surveillance Medical Officers was conducted. Data reported at the national level are analysed and distributed weekly. Annualized rates of non-polio AFP increased from 0.22 per 100,000 children aged under 15 years in 1997 to 1.39 per 100,000 in 1999. The proportion of cases with two adequate stools collected within two weeks of the onset of paralysis increased from 34% in 1997 to 68% in 1999. The number of polio cases associated with the isolation of wild poliovirus decreased from 211 in the first quarter of 1998 to 77 in the first quarter of 1999. Widespread transmission of wild poliovirus types 1 and 3 persists throughout the country; type 2 occurs only in Bihar and Uttar Pradesh. In order to achieve polio eradication in India during 2000, extra national immunization days and house-to-house mopping-up rounds should be organized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".