A Narrative Review of Surgery for Prolactinomas: Considerations and Controversies
Bibliographic record
Abstract
For several decades, dopamine agonist therapy has been the mainstay of treatment for prolactinomas, with surgery generally considered a second line for cases failing medical therapy due to intolerance or resistance. There is increasing recognition of the burden of long-term DA therapy; many patients experience debilitating side effects, and emerging evidence demonstrates that the prevalence of impulse control disorders has been vastly underreported. Long-term DA therapy is associated with significant costs to patients and healthcare systems, which is projected to exceed that of surgery in many circumstances. Recent advancements in surgical approaches, including endoscopic transsphenoidal surgery, have led to improved surgical outcomes (82-100% remission rates; serious complication rates < 2%), prompting a reappraisal of the role of surgery for prolactinoma. Favourable surgical outcomes have been observed in both remission and complication rates for microprolactinomas and well-circumscribed macroprolactinomas, leading to consideration of surgery as an earlier, or first-line, option in the treatment paradigm. Potential advantages of surgical management should be weighed against institutional case volume and expertise, the risk of perioperative complications, and the need for adjuvant medical therapy post-operatively. Ultimately, patients and care-providers should engage in shared decision-making following informed discussion about the risks and benefits of both medical and surgical approaches.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".