Pheochromocytoma and paraganglioma: time and space are only part of the essence
Bibliographic record
Abstract
We thank Dr Ebbehøjfor and colleagues (1) for their enthusiasm for our recent epidemiological study on the incidence of pheochromocytoma and paraganglioma (PPGL) in Alberta, Canada (2), along with their interest in our follow-up analysis showing an increased annual incidence of disease with higher altitude from selected studies (3). In their letter, they conducted an updated search of the literature, analyzed the available data according to annual incidence at the time of observation, and demonstrated that the frequency of detected PPGL has also increased over time. The findings presented by Dr Ebbehøjfor and colleagues are important contributions that help to advance our understanding of the epidemiology of PPGL. We point out that their interpretation of the data is not fundamentally different than our own. We previously stated that although variations in altitude account for a small degree of the between-study differences observed (a finding that they also corroborate), other factors such as variable disease detection methods, data sources, and study quality are likely more important sources of statistical heterogeneity (2, 3). We also acknowledged the apparent rise in PPGL frequency with more recent data and further postulated that it was mediated, at least in part, through similar mechanisms (i.e. increased global awareness of these tumors with improvements in diagnostic practices over time) (2), as others have likewise suggested (4).
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".