Performance of acute flaccid paralysis surveillance compared with <scp>W</scp>orld <scp>H</scp>ealth <scp>O</scp>rganization standards
Bibliographic record
Abstract
AIM: To compare acute flaccid paralysis (AFP) surveillance systems used by members of the International Network of Paediatric Surveillance Units (INoPSU) across the five AFP surveillance performance indicators recommended by the World Health Organization (WHO) for the maintenance of polio-free certification. METHODS: A survey was administered to AFP surveillance co-ordinators in five INoPSU member countries (Australia, Belgium, Canada, New Zealand and Switzerland). Data collected included information on surveillance system processes, WHO-recommended performance indicators, investigative practices and final diagnoses of cases from 2006 to 2010. RESULTS: All countries contacted completed the survey. Each country used similar case definitions and processes for collecting AFP data. All countries used at least one of the WHO indicators for surveillance. No country consistently met the performance indicator for incidence or stool sampling. In all countries, at least one form of neurological testing was used to diagnose cases of AFP. Guillain-Barré syndrome was the most common final diagnosis in all countries for all years examined. CONCLUSIONS: Industrialised countries surveyed do not consistently meet the WHO-recommended AFP surveillance performance indicators. An opportunity exists for INoPSU to suggest a standard way for member countries to collect AFP data in order to examine the potential for strengthening the current systems or introducing additional enterovirus surveillance or alternative/complementary neurological performance measures suitable for countries that have eliminated polio. INoPSU member countries are evaluating these possibilities.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".