Natural history of nonfunctioning pituitary microadenomas: a systematic review and individual participant data meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Increased frequency of neuroimaging has led to enhanced identification of small nonfunctioning pituitary adenomas (NFPAs) leading, in many cases, to extensive follow-up. However, the value of ongoing monitoring of these incidental lesions remains unclear. The study aims to determine the need for surgical intervention and assess the risks of developing new endocrinopathies during follow-up in patients with conservatively treated micro-NFPAs. DESIGN: A systematic review and individual participant data (IPD) meta-analysis. METHODS: We conducted a bibliographical search of PubMed and EMBASE to identify relevant studies. Authors of eligible studies were invited to share IPD. Cohort studies including patients with conservatively treated micro-NFPAs with at least 1 follow-up magnetic resonance imaging were considered eligible. Fourteen studies met inclusion criteria. Six authors provided IPD (N = 647). Data were reanalyzed for verification. In cases of discrepancies the original authors were contacted for authentication. RESULTS: Risk estimates were reported as number of events per 100 person-years (PYs). Estimates were pooled using the 2-step approach. Overall probability of surgery was 0.2/100 PYs (95% CI: 0.0-0.4; I2 = 28%). Probability of surgery due to visual impairment was 0.1/100 PYs (95% CI: 0.0-0.2; I2 = 0%). Both were independent of baseline tumor size (≥6 or <6 mm), sex, or age (P values >.40). Risk of developing a new endocrinopathy was 1.0/100 PYs (95% CI: 0.4-1.6; I2 = 0%). Data for classical meta-analysis were available for 7 studies (N = 1089) and supported the IPD results. CONCLUSIONS: These data suggest that routine follow-up of micro-NFPAs can be reduced significantly and that available guidelines should be revisited.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.003 | 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".