The Economic Implications of Three Biochemical Screening Algorithms for Pheochromocytoma
Bibliographic record
Abstract
Pheochromocytoma is a rare, life-threatening condition. Using a modeling technique, we studied the economic implications of detection strategies for pheochromocytoma (third-party payer perspective). The diagnostic efficacy of biochemical tests was based on Mayo Clinic Rochester data. In all hypothetical algorithms, positive biochemical tests were followed by abdominal computerized tomography and, if negative, metaiodobenzylguanidine scintigraphy. In each hypothetical algorithm, imaging would be indicated after positive biochemical testing as follows: algorithm A, fractionated plasma metanephrine measurements above the laboratory reference range; or algorithm B, abnormal measurements of 24-h urinary total metanephrines or catecholamines. In algorithm C, subjects with fractions of plasma metanephrine at or above 0.5 nmol/liter or normetanephrine at or above 1.80 nmol/liter would undergo imaging, whereas those with values between the reference range and these cutoffs would undergo 24-h urinary measurements (total metanephrines and fractionated catecholamines) and be imaged if positive. We determined that, if 100,000 hypertensive patients (including 500 patients with pheochromocytoma) were tested, algorithm A (measurement of fractionated plasma metanephrines alone) would detect 489 pheochromocytoma patients at a cost of 56.6 million dollars, whereas B (24-h urinary measurements) would detect 457 pheochromocytoma patients for 39.5 million dollars, and C (combination of measurements of fractionated plasma metanephrines and urines) would detect 478 patients for 28.6 million dollars. None of the screening strategies for pheochromocytoma described are affordable if implemented on a routine basis in extremely low-risk patients. However, algorithm C may be the least costly, and at a reasonable level of sensitivity, for subjects in whom the suspicion of disease is moderate.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".