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 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".