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Record W4411777718 · doi:10.1210/jcemcr/luaf136

High-dose Hook Effect in a Case of Giant Prolactinoma Confounded by Acute Kidney Injury

2025· article· en· W4411777718 on OpenAlexaff
Anna Lam, Connie Prosser, Constance L. Chik

Bibliographic record

VenueJCEM Case Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineProlactinomaProlactinMagnetic resonance imagingAcute kidney injuryCabergolineInternal medicineUrologySurgeryHormoneRadiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.281
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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