A comparative, population-based analysis of pituitary incidentalomas vs clinically manifesting sellar masses
Bibliographic record
Abstract
Purpose Sellar masses may present either with clinical manifestations of mass effect/hormonal dysfunction (CMSM) or incidentally on imaging (pituitary incidentaloma (PI)). This novel population-based study compares these two entities. Methods Retrospective analysis of all patients within a provincial pituitary registry between January 2006 and June 2014. Results Nine hundred and three patients were included (681 CMSM, 222 PI). CMSM mainly presented with secondary hormone deficiencies (SHDs) or stalk compression (29.7%), whereas PIs were found in association with neurological complaints (34.2%) ( P < 0.0001). PIs were more likely to be macroadenomas (70.7 vs 49.9%; P < 0.0001). The commonest pathologies among CMSM were prolactinomas (39.8%) and non-functioning adenomas (NFAs) (50%) in PI ( P < 0.0001). SHDs were present in 41.3% CMSM and 31.1% PI patients ( P < 0.0001) and visual field deficit in 24.2 and 29.3%, respectively ( P = 0.16). CMSM were more likely to require surgery (62.9%) than PI (35.8%) ( P < 0.0005). The commonest surgical indications were impaired vision and radiological evidence of optic nerve compression. Over a follow-up period of 5.7 years for CMSM and 5.0 years for PI, tumour growth/recurrence occurred in 7.8% of surgically treated CMSM and 2.6% without surgery and PI, 0 and 4.9%, respectively ( P = 1.0). There were no significant differences in the risk of new-onset SHD in CMSM vs PI in those who underwent surgery ( P = 0.7) and those who were followed without surgery ( P = 0.58). Conclusions This novel study compares the long-term trends of PI with CMSM, highlighting the need for comprehensive baseline and long-term radiological and hormonal evaluations in both entities.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".