“Hook Effect” in Calcitonin Immunoradiometric Assay in Patients with Metastatic Medullary Thyroid Carcinoma: Case Report and Review of the Literature
Bibliographic record
Abstract
CONTEXT: Calcitonin is a well-established tumor marker for medullary thyroid carcinoma (MTC). Because surgery is the only effective treatment for patients with MTC, the postoperative level of serum calcitonin will dictate whether residual disease was left behind and whether reintervention is necessary. RESULTS: We describe here the case of a 41-yr-old man with metastatic MTC. Despite extensive disease in the neck as well as metastatic lesions in the liver, his serum calcitonin, measured with a commercial one-step immunoradiometric assay, was only minimally elevated (244 ng/liter). After serial dilutions, a nonlinear relationship became evident, suggesting the presence of a "hook effect." Treatment of the serum with heterophilic blocking reagent revealed no change. Calcitonin was then measured with a different immunoradiometric assay and revealed a much higher level. Similar discrepancies were found in different samples from various patients when analyzed with different calcitonin immunoassays. CONCLUSION: To our knowledge, this is the first reported case of a phenomenon such as the hook effect in a calcitonin immunoradiometric assay in patients with MTC. Being aware of this phenomenon is important, because a low calcitonin result could give false reassurance to both the patient and the clinician and could dramatically change the prognosis of the patient.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".