The Canadian Paediatric Surveillance Program: Beyond collecting numbers
Bibliographic record
Abstract
Traditionally, anecdotal data and retrospective case reports have been used for insight into the natural history, epidemiology and case management of rare diseases. This lack of information has often resulted in delayed recognition and diagnosis of rare diseases, increasing the risk of complications or death of children. Furthermore, the study of rare condiions has been hampered by the need to generate sufficient numbers to enable meaningful analysis and interpretation, a need that requires data collection from a large population. The Canadian Paediatric Surveillance Program (CPSP) was established in 1996 to contribute to the improvement of the health of children and youth by national surveillance and research into uncommon paediatric diseases and conditions. The CPSP provides the mechanism to enable the prospective collection of national epidemiological data on such diseases and conditions. After five years, has the CPSP risen to meet expectations? Is it based on scientific evidence? The CPSP has revealed itself to be a very sensitive surveillance tool, providing invaluable longitudinal, epidemiological information for public health decision-makers. The present paper reviews how the different communicable diseases on the CPSP monthly reporting form stand the test of the 1998 priority criteria for diseases under national surveillance set by Canada's Advisory Committee on Epidemiology.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".