Hyperprolactinemia: An Unfamiliar Acquired Cause of Pulmonary Embolism
Bibliographic record
Abstract
Despite our knowledge of congenital and acquired hypercoagulable states, many unprovoked venous thromboemboli remain idiopathic. High prolactin levels may add diagnostic differential to these unexplained thrombi. We report a case of prolactinemia as a potential cause of a pulmonary embolus (PE) and review the literature to elucidate the connection between high prolactin levels and thrombosis. A 68-year-old female presented with dyspnea and frontal headaches. Exam showed tachycardia, tachypnea, and hypoxemia with an oxygen saturation of 91% on room air. Labs were positive for an elevated troponin. Imaging with a computed tomography (CT) angiogram of the chest and an echocardiogram confirmed a diagnosis of sub-massive PE. A CT of the head showed a mass lesion in the pituitary fossa. A hypercoagulable workup was negative and pituitary hormone levels were within normal limits aside from a high prolactin of 270.9 ng/mL. In our patient, high prolactin secondary to a prolactinoma remained the main culprit in her venous thromboembolism given a thorough negative history and workup for other etiologies. Literature investigating prolactin’s effect on adenosine diphosphate-mediated platelet aggregation along with clinical scenarios in which elevated prolactin is the only sound explanation for a venous thrombosis may make it a risk factor which should be checked following a patient history indicative of a prolactin derangement. J Med Cases. 2016;7(11):491-492 doi: http://dx.doi.org/10.14740/jmc2651w
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".