Baseline MRI findings as predictors of hypopituitarism in patients with non-functioning pituitary adenomas
Bibliographic record
Abstract
Hypopituitarism tends to occur in large pituitary adenomas. However, similar tumors could present with strikingly different hormonal deficiencies. In this study, we looked at MRI characteristics in non-functioning pituitary adenomas (NFPA), which could predict secondary adrenal insufficiency (SAI) and central hypothyroidism (CHT). We reviewed the files of patients with NFPA attending our clinic. Tumor size, invasiveness, MR-signal intensity, and gadolinium enhancement in preoperative MRI were recorded along with documented presurgical hypopituitarism profile. Logistic regression was used to predict SAI, CHT, or both (SAI/CHT) based on MRI and demographic parameters. Receiver operating characteristic curves were used to determine their diagnostic utility. One hundred twenty-one patients were included in the study. Older age (P = 0.021), male sex (P = 0.043), stalk deviation (P < 0.0001), contrast enhancement (P = 0.029), and optic chiasma compression (P = 0.012) were associated with SAI/CHT. Adenoma vertical height, largest diameter, and estimated volume were also strongly associated with SAI/CHT (P < 0.0001). These associations remained significant in a multivariate analysis. No tumor smaller than 12 mm in vertical height, 17 mm in largest diameter, or 0.9 cm3 in volume was associated with SAI/CHT. At cut-off ≥18 mm for vertical height, ≥23 mm for largest diameter, and ≥3.2 cm3 the sensitivity was around 90-92% for detecting SAI/CHT. Only vertical height was significantly associated with any one or more pituitary hormonal deficit (P = 0.001). In conclusion, adenoma size, independent of the measurement used, remains the best predictor of SAI/CHT in NFPA. Dynamic testing to rule out SAI is probably indicated in adenomas larger than 18 mm vertical height, 23 mm largest diameter and 3.2 cm3 adenoma volume.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".