The importance of measuring prolactin prior to surgical management of a pituitary lesion: An illustrative case
Bibliographic record
Abstract
The characterization of sellar and suprasellar lesions is reliant on patient presentation, medical imaging, and hormone profiling. Prolactinomas are the most common type of functional pituitary adenomas, accounting for up to 57%. Importantly, prolactinomas can present without clear symptoms and with doubtful or even normal imaging. A 41-year-old male patient was referred to neurosurgery for consideration for resection of a sellar lesion, as initial CT imaging suggested a large meningioma. Subsequent MRI of the sella favored macroadenoma, meningioma, and craniopharyngioma as the top differential considerations. These conditions all indicate a diagnosis that would require surgical management. Clinical evaluation of this patient did not elicit any obvious clinical features suggestive of hyperprolactinemia. Fortunately, we obtained a full hormone panel which revealed a significantly elevated prolactin level of 17,390 µg/L. Based on this elevated prolactin level, we diagnosed a pituitary giant prolactinoma. Treatment with a dopamine agonist therapy was initiated and the response confirmed this diagnosis. This case demonstrates the importance of obtaining a prolactin level prior to surgical management of a sellar lesion. Had a prolactin level not been obtained, this patient would have undergone surgical resection based on both the imaging and clinical judgment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| 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".