Rare disease surveillance: An international perspective
Bibliographic record
Abstract
BACKGROUND: The International Network of Paediatric Surveillance Units (INoPSU) was established in 1998 and met formally for the first time in Ottawa, Ontario in June 2000. OBJECTIVES: To document the methodology and activities of existing national paediatric surveillance units; the formation of INoPSU; the diseases studied by INoPSU members; and the impact of such studies on education, public health and paediatric practice. METHODS: Directors of paediatric surveillance units in Australia, Britain, Canada, Germany, the Netherlands, Latvia, Malaysia, Papua New Guinea, New Zealand and Switzerland were asked to provide information on each unit’s affiliations, funding and staffing; the method of case ascertainment, the mailing list and response rates; and diseases studied. Original articles that reported data derived from units were identified by a search of an electronic database (MEDLINE), and additional information was obtained from units’ annual reports. RESULTS: Worldwide, 10 units (established from 1986 to 1997), use active national surveillance of more than 8500 clinicians each month to identify cases of rare or uncommon diseases in a childhood population (younger than 15 years of age) of over 47 million (monthly response rate 73% to 98%). By January 1999, units had initiated 147 studies on 103 different conditions, and 63 studies were completed. CONCLUSION: INoPSU enhances collaboration among units from four continents, providing a unique opportunity for simultaneous cross-sectional studies of rare diseases in populations with diverse geographical and ethnic characteristics. It facilitates the sharing of ideas regarding current methodology, ethics, the most appropriate means of evaluating units and their potential application.
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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".