Properly Collected Plasma Metanephrines Excludes PPGL After False-Positive Screening Tests
Bibliographic record
Abstract
CONTEXT: False-positive results are common for pheochromocytoma/paraganglioma (PPGL) real-world screening. OBJECTIVE: Determine the correlation between screening urine and seated plasma metanephrines in outpatients where PPGL was absent, compared to meticulously prepared and supine-collected plasma metanephrines with age-adjusted references. DESIGN: Retrospective cohort study. SETTING: Databases from a single-provider provincial laboratory (2012-2018), a validated PPGL registry, and a manual chart review from a specialized endocrine testing unit. PATIENTS: PPGL registry data excluded known PPGL cases from the laboratory database. Outpatients having both urine and plasma metanephrines <90 days apart. METHODS: The correlation between urine and seated plasma measures along with the total positivity rate. All cases of plasma metanephrines drawn in the endocrine unit were reviewed for test indication and test positivity rate. RESULTS: There were 810 non-PPGL pairs of urine and plasma metanephrines in the laboratory database; 46.1% of urine metanephrines were reported high. Of seated outpatient plasma metanephrines drawn a median of 5.9 days later, 19.2% were also high (r = 0.33 and 0.50 for normetanephrine and metanephrine, respectively). In contrast, the meticulously prepared and supine collected patients (n = 139, 51% prior high urine metanephrines) had <3% rate of abnormal high results in patients without known PPGL/adrenal mass. CONCLUSIONS: There was a poor-to-moderate correlation between urine and seated plasma metanephrines. Up to 20% of those with high urine measures also had high seated plasma metanephrines in the absence of PPGL. Properly prepared and collected supine plasma metanephrines had a false-positive rate of <3% in the absence of known PPGL/adrenal mass.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".