Analysis and natural history of pituitary incidentalomas
Bibliographic record
Abstract
OBJECTIVES: Pituitary incidentalomas (PI) are frequently found on brain imaging. Despite their high prevalence, little is known about their long-term natural history and there are limited guidelines on how to monitor them. METHODS: We conducted a retrospective study to compare epidemiological characteristics at presentation and the natural history of PI in population-based vs referral-based registries from two tertiary-care referral centers in Canada. RESULTS: A total of 328 patients with PI were included, of whom 73% had pituitary adenomas (PA) and 27% had non-pituitary sellar masses. The commonest indications for imaging were headache (28%), dizziness (12%) and stroke/transient ischemic attack (TIA) (9%). There was a slight female preponderance (52%) with a median age of 55 years at diagnosis; 71% presented as macroadenomas (>10mm). Of PA, 25% were functioning tumors and at presentation 36% of patients had evidence of secondary hormonal deficiency (SHD). Of the total cohort, 68% were treated medically or conservatively whereas 32% required surgery. Most tumors (87% in non-surgery and 68% in post-surgery group) remained stable during follow-up. Similarly, 84% of patients in the non-surgery and 73% in the surgery group did not develop additional SHD during follow-up. The diagnosis of non-functioning adenoma was a risk factor for tumor enlargement and a change in SHD status was associated with a change in tumor size. CONCLUSIONS: Our data suggest that most PI seen in tertiary-care referral centers present as macroadenomas and may frequently be functional, often requiring medical or surgical intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".