Surgical Interventions and Medical Treatments in Treatment-Naïve Patients With Acromegaly: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
CONTEXT: Acromegaly is usually treated with surgery as a first-line treatment, although medical therapy has also been used as an alternative primary treatment. OBJECTIVE: We conducted a systematic review and meta-analysis to synthesize the existing evidence comparing these two approaches in treatment-naïve patients with acromegaly. DATA SOURCES: This study performed a comprehensive search in multiple databases, including Medline, EMBASE, and Scopus from early inception through April 2014. STUDY SELECTION: The study used original controlled and uncontrolled studies that enrolled patients with acromegaly to receive either surgical treatment or medical treatment as their first-line treatment. DATA EXTRACTION: Reviewers extracted data independently and in duplicates. Because of the noncomparative nature of the available studies, we modified the Newcastle-Ottawa Scale to assess the quality of included studies. Outcomes evaluated were biochemical remission and change in IGF-1 or GH levels. We pooled outcomes using the random-effects model. DATA SYNTHESIS: The final search yielded 35 studies enrolling 2629 patients. Studies were noncomparative series with a follow-up range of 6-360 months. Compared with medical therapy, surgery was associated with a higher remission rate (67% vs 45%; P = .02). Surgery had higher remission rates at longer follow-up periods (≥ 24 mo) (66% vs 44%; P = .04) but not the shorter follow-up periods (≤ 6 mo) (37% vs 26%; P = .22) [Corrected].Surgery had higher remission rates in the follow-up levels of GH (65% vs 46%; P = .05). In one study, the IGF-1 level was reduced more with surgery compared with medical treatment (-731 μg/L vs -251 μg/L; P = .04). Studies in which surgery was performed by a single operator reported a higher remission rate than those with multiple operators (71% vs 47%; P = .002). CONCLUSIONS: Surgery may be associated with higher remission rate; however, the confidence in such evidence is very low due to the noncomparative nature of the studies, high heterogeneity, and imprecision.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".