Incidence of pheochromocytoma and paraganglioma varies according to altitude: meta-regression analysis
Bibliographic record
Abstract
We thank Drs Patel and Mihai for their interest in our article where we described the incidence of pheochromocytoma and paraganglioma in Alberta, Canada (1). In their letter (2), they raised important points related to the role of hypoxia in tumorigenesis, highlighting the supporting evidence to date. Exposure to high altitude may indeed be a modifiable risk factor for pheochromocytoma and paraganglioma. However, it is admittedly difficult to examine the epidemiology of this association on a large population scale because of the inherent challenges in accurately quantifying the cumulative risk and the intensity of exposure over the period of a person's lifetime (e.g. for those who live at different altitudes throughout their lives) (3). Accordingly, as Drs Patel and Mihai point out, the best available evidence may be derived from comparisons of existing studies that were conducted in populations of different altitudes. Addressing this, we examined the global incidence of pheochromocytoma and paraganglioma across available studies, accounting for differences in altitude and barometric pressure (Table 1). To facilitate comparisons, the annual incidence proportions per 100 000 people along with corresponding 95% CIs were extracted, if possible (or otherwise manually calculated) (4). The altitude of the population was based on the average elevation of the geographic location of the study (https://en-ca.topographic-map.com; accessed March 2, 2021) and the corresponding barometric pressure (PB) in Torr estimated using the Model Atmosphere equation (5): PB = e(6.63268 − 0.1112 × (altitude in km) − 0.00149 × (altitude in km) × (altitude in km)). We then conducted a meta-analysis using restricted maximum likelihood estimates with a random-effects model. Statistical heterogeneity was assessed and quantified using the Cochran Q test and I2 statistic, respectively (6). We explored potential explanations for between-study heterogeneity using meta-regression.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.020 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".