Pitfalls in Diagnostic Gastrin Measurements
Bibliographic record
Abstract
BACKGROUND: Gastrin measurements are performed primarily for the diagnosis of gastrin-producing tumors, gastrinomas, which cause the Zollinger-Ellison syndrome (ZES). Gastrin circulates as several bioactive peptides, however, and the peptide pattern in gastrinoma patients often deviates from normal. Therefore, it is necessary to measure all forms of gastrin. CONTENT: Only immunoassays are useful for measurement of gastrin in plasma. The original assays were RIAs developed in research laboratories that used antibodies directed against the C terminus of gastrin peptides. Because the C-terminal tetrapeptide amide sequence constitutes the active site of gastrin peptides, these assays were well suited for gastrinoma diagnosis. More recently, however, most clinical chemistry laboratories have switched to commercial kits. Because of recent cases of kit-measured normogastrinemia in patients with ZES symptoms, the diagnostic sensitivity and analytical specificity of the available kits have been examined. The results show that gastrin kits frequently measure falsely low concentrations because they measure only a single gastrin form. Falsely high concentrations were also encountered, owing to overreactivity with O-sulfated gastrins or plasma proteins. Thus, more than half of the gastrin kits on the market are unsuited for diagnostics. SUMMARY: Gastrinomas are neuroendocrine tumors, some of which become malignant. A delay in diagnosis leads to fulminant ZES, with major, even lethal, complications. Consequently, it is necessary that the diagnostic sensitivity of gastrin kits be adequate. This diagnostic sensitivity requires antibodies that bind the C-terminal epitope of bioactive gastrins without the influence of O-sulfation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".