Time Course of Juvenile Onset Recurrent Respiratory Papillomatosis Caused by Human Papillomavirus
Bibliographic record
Abstract
BACKGROUND: With the recent licensure of a new quadrivalent vaccine, many diseases caused by human papillomavirus (HPV) can now be prevented, including recurrent respiratory papillomatosis (RRP). The purpose of this study was to describe the burden and time course of juvenile onset RRP. METHODS: A retrospective chart review was conducted of children with airway papillomatosis at the Hospital for Sick Children in Toronto, Canada, between 1994 and 2004. Statistical methods included descriptive statistics of the cohort, a repeated events survival model, and nonlinear modeling equations to describe the time course of illness. RESULTS: Nine hundred twenty-six surgical procedures in 67 patients were identified through a review of surgical records. The median age at diagnosis was 3.2 years (range, 0.1-14.8 years) and the most common presenting symptom was hoarseness (75%). Adjuvant pharmacologic therapy (interferon or cidofovir) was used in 13 cases (19%). HPV types 6 or 11 were identified most commonly as the etiologic agent. Nonlinear modeling equations (exponential and quadratic) fit the observed data well, and were superior to linear models. Repeated events survival analysis identified significant prognostic variables: surgeon, adjuvant therapy, and anatomic score. A decision rule is presented that allows the time to next surgery to be predicted based on the previous surgery and the anatomic score. CONCLUSIONS: Most patients have a decelerating rate of debulking surgeries over time, well described by our nonlinear modeling equations. Factors affecting the time course of RRP include: inter-surgeon variability, the extent and severity of papillomas at the time of laryngoscopy, and the use of adjuvant medical therapies.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".