High-dose Hook Effect in a Case of Giant Prolactinoma Confounded by Acute Kidney Injury
Bibliographic record
Abstract
A 21-year-old man admitted to the hospital for acute kidney injury developed blindness 5 days after admission. Workup included magnetic resonance imaging, which was terminated prematurely due to a ball bearing/air gun pellet in his neck causing pain. His incomplete scan revealed a large sellar mass causing significant compression of the optic chiasm. Hormone testing showed low levels of IGF-1 and testosterone. A mildly elevated prolactin (PRL) level was attributed to stalk effect and decreased kidney function. For treatment, the patient underwent transsphenoidal tumor resection. Unexpectedly, pituitary immunohistochemistry revealed a prolactinoma. Further inquiry corroborated a history of headache, hypogonadal symptoms, and gynecomastia. He was started on a dopamine agonist with improvement in his vision and hormone levels; however, PRL was further increased. In retrospect, the mildly elevated PRL on initial testing was caused by the high-dose hook effect whereby excessively high PRL levels result in erroneously low measurements by immunoassays. Thus, in patients presenting with large sellar masses and normal/mildly elevated PRL, the sample should be diluted to exclude high-dose hook effect and ensure an accurate level. In addition, questioning of metallic fragments in plain language may help prevent magnetic resonance imaging injury.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".