Bibliographic record
Abstract
After infancy, galactorrhea usually is medication-induced. The most common pathologic cause of galactorrhea is a pituitary tumor. Other causes include hypothalamic and pituitary stalk lesions, neurogenic stimulation, thyroid disorders, and chronic renal failure. Patients with the latter conditions may have irregular menses, infertility, and osteopenia or osteoporosis if they have associated hyperprolactinemia. Tests for pregnancy, serum prolactin level and serum thyroid-stimulating hormone level, and magnetic resonance imaging are important diagnostic tools that should be employed when clinically indicated. The underlying cause of galactorrhea should be treated when possible. The decision to treat patients with galactorrhea is based on the serum prolactin level, the severity of galactorrhea, and the patient's fertility desires. Dopamine agonists are the treatment of choice in most patients with hyperprolactinemic disorders. Bromocriptine is the preferred agent for treatment of hyperprolactin-induced anovulatory infertility. Although cabergoline is more effective and better tolerated than bromocriptine, it is more expensive, and treatment must be discontinued one month before conception is attempted. Surgical resection rarely is required for prolactinomas.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".