Epidemiology of pheochromocytoma and paraganglioma: population-based cohort study
Bibliographic record
Abstract
OBJECTIVE: Despite the significant morbidity and mortality associated with pheochromocytoma and paraganglioma, little is known about their epidemiology. The primary objective was to determine the incidence of pheochromocytoma and paraganglioma in an ethnically diverse population. A secondary objective was to develop and validate algorithms for case detection using laboratory and administrative data. DESIGN: Population-based cohort study in Alberta, Canada from 2012 to 2019. METHODS: Patients with pheochromocytoma or paraganglioma were identified using linked administrative databases and clinical records. Annual incidence rates per 100 000 people were calculated and stratified according to age and sex. Algorithms to identify pheochromocytoma and paraganglioma, based on laboratory and administrative data, were evaluated. RESULTS: A total of 239 patients with pheochromocytoma or paraganglioma (collectively with 251 tumors) were identified from a population of 5 196 368 people over a period of 7 years. The overall incidence of pheochromocytoma or paraganglioma was 0.66 cases per 100 000 people per year. The frequency of pheochromocytoma and paraganglioma increased with age and was highest in individuals aged 60-79 years (8.85 and 14.68 cases per 100 000 people per year for males and females, respectively). An algorithm based on laboratory data (metanephrine >two-fold or normetanephrine >three-fold higher than the upper limit of normal) closely approximated the true frequency of pheochromocytoma and paraganglioma with an estimated incidence of 0.54 cases per 100 000 people per year. CONSLUSION: The incidence of pheochromocytoma and paraganglioma in an unselected population of western Canada was unexpectedly higher than rates reported from other areas of the world.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".