Radiological Growth Patterns of Prolactinomas and Nonfunctioning Adenomas
Bibliographic record
Abstract
OBJECTIVES: To compare growth patterns of nonfunctioning and prolactin-producing pituitary macroadenomas, and to find whether their specific growth patterns are associated with clinically significant effects on vision. MATERIALS AND METHODS: From our comprehensive provincial neuropituitary registry, we retrospectively identified 35 randomly selected patients each with nonfunctioning adenomas and prolactinomas >10 mm in any dimension. MRI scans were analyzed to determine the superior and inferior growth, volume, and maximum craniocaudal height of the adenomas. Patients underwent visual field testing at diagnosis. Continuous variables were compared using Student's t test, the Mann-Whitney U test, and ANOVA. Categorical variables were compared using the chi-square test. RESULTS: The mean height of prolactinomas (23.2±11.3 mm) was similar to nonfunctioning adenomas (22.3±9.3 mm, p=0.8), and so were mean tumor volumes (prolactinoma=5.9±8 ml vs. nonfunctioning adenoma=4.8±5 ml, p=0.47). However, the mean suprasellar growth for prolactinomas was 2.9±5.3 mm and 7.3±4.7 mm for nonfunctioning adenomas (p<0.001), and the mean infrasellar growth was 10.2±8.0 and 5.0±6.6 mm, respectively (p=0.04). The inferior growth pattern of prolactinomas was associated with a significantly lower likelihood of having visual field abnormalities (11.4 vs. 57.1%, p<0.001). CONCLUSIONS: Prolactinomas have predominantly inferior growth compared to nonfunctioning adenomas and are less likely to cause vision changes.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".