Medication versus Early Surgery in Prolactinomas with Visual Involvement: Time for Randomized Controlled Trials
Bibliographic record
Abstract
Background: Visual morbidity due to optic compressive neuropathy is common in patients with large prolactinomas. Although medical therapy has been the mainstay of treatment for these patients, some researchers have argued that early surgery leads to better visual outcomes by more rapidly decompressing the optic system than medicines. We performed a systematic review to compare the visual outcomes of patients with macroprolactinomas treated medically versus surgically to better understand if there is equipoise to support a randomized controlled trial in the field. Methods: A systematic review was performed using PRISMA Guidelines. PubMed, Embase, and Medline were searched from inception to September 2, 2024, for primary articles that reported visual outcomes in adult patients with macroprolactinoma treated with either medication, surgery, or both. A quantitative and qualitative analysis was used to compare visual outcomes between treatment methods. Results: Of 15 eligible studies in the final analysis, all were case series and of moderate or high risk of bias. 3 investigated surgery (TSS), 3 oral bromocriptine (BRC), 3 injectable bromocriptine (BRC-LAR), 4 cabergoline (CAB) and 2 quinagolide (CV). Visual defect (VD) resolution was highest in and comparable between surgery (25/30, 83%), CAB (134/163, 82%) and BRC-LAR (13/15, 87%). VDs resolved at lower rates in patients treated with oral BRC (15/31, 48%) and CV (4/7, 57%). There were no direct controlled comparisons of surgery and medical therapy and considerations of risk–benefit ratios or long-term economic implications was limited. Discussion: Reported visual outcomes are similar between surgery and the best medical therapies; however, direct controlled comparisons are lacking. Given the similarity in outcomes and rapidly improving surgical approaches, randomized controlled trials assessing visual outcomes between surgery and medical therapy in patients with large prolactinomas are justified to better delineate the role of each modality of treatment for these patients. Publication History Article published online: 07 February 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".