Giant Prolactinomas: Case Series and Review of the Literature
Bibliographic record
Abstract
Giant prolactinomas are rare and constitute only 2-3% of prolactinomas. They are defined as prolactinomas greater than 4 cm in size with a prolactin level of > 1,000 µg/L. Unlike prolactinomas, giant prolactinomas have a male preponderance and present a decade earlier in men as compared to women. Giant prolactinomas may present with galactorrhea, irregular periods or decreased libido. Due to their large size, they can involve surrounding brain structures and may present with hydrocephalus, dizziness, seizures, deafness, and cognitive dysfunction. Laboratory assessment may reveal a falsely low prolactin level secondary to “hook effect” which is due to saturation of the capture and detection antibodies used in the assay. Dilution of the sample would show a paradoxical increase in prolactin levels confirming the hook effect. Magnetic resonance imaging (MRI) is warranted to assess the extent of the tumor. Dopamine agonists are the treatment of choice in giant prolactinomas and lead to rapid resolution of symptoms, normalization of prolactin levels, and reduction in the size of the tumors. Refractory giant prolactinomas may be treated with surgery, temozolomide or radiotherapy. Giant prolactinomas can pose unique diagnostic and management challenges because of atypical presentations and confounding laboratory assessments. We present four cases of giant prolactinomas each presenting in a unique manner and discuss the diagnostic and management dilemmas associated with them. J Endocrinol Metab. 2020;10(6):182-189 doi: https://doi.org/10.14740/jem652
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".