The epidemiology of juvenile onset recurrent respiratory papillomatosis derived from a population level national database
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To develop a national database of cases of juvenile onset recurrent respiratory papillomatosis (JoRRP) in Canada, to calculate trends in incidence and prevalence of JoRRP from January 1994 to December 2007 at the national and regional level, and to mathematically model the natural history of JoRRP. STUDY DESIGN: Retrospective, multicenter study. METHODS: Patient demographics, clinical presentation, human papillomavirus status, method and timing of treatment, and indicators of disease severity were captured with a standardized case report form. Operative records were retrospectively scored using the Derkay-Coltrera staging system for each operative intervention. Trends in incidence and prevalence of JoRRP from 1994 to 2007 were calculated at a national and regional level using national population census data. A multivariable mixed effects linear model was used to explore the effect of surgery-specific variables on the intersurgical interval. Nonlinear least-squares regression was used to model the natural history of JoRRP. RESULTS: Development of a national database of children with JoRRP identified 243 cases who underwent 3,021 surgical procedures. The national incidence of JoRRP from 1994 to 2007 was 0.24 per 100,000 children aged 14 years and younger. The prevalence was 1.11 per 100,000 children. The natural history of JoRRP followed a nonlinear time course with 64% of cases having a decreasing annual rate of surgery over time. CONCLUSIONS: A Canadian national database of children with JoRRP was successfully developed. Modeling of the natural history of JoRRP may have important clinical and research implications.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".