The impact of transsphenoidal surgery on pituitary function in patients with non-functioning macroadenomas
Bibliographic record
Abstract
PURPOSE: Transsphenoidal surgery for non-functioning pituitary adenomas (NFPAs) can alter pituitary function. We assessed the rates of improvement and deterioration of pituitary function by axis and searched for predictive factors of these outcomes. METHODS: We reviewed consecutive medical files from patients having had transsphenoidal surgery for NFPA between 2004 and 2018. Pituitary functions and MRI imaging were analyzed prior and after surgery. The occurrence of recovery and new deficit were documented per axis. Prognostic factors of hormonal recovery and new deficits were searched. RESULTS: Among 137 patients analyzed, median tumor size of the NFPA was 24.8 mm and 58.4% of patients presented visual impairment. Before surgery, 91 patients (67%) had at least one abnormal pituitary axis (hypogonadism: 62.4%; hypothyroidism: 41%, adrenal insufficiency: 30.8%, growth hormone deficiency: 29.9%; increased prolactin: 50.8%). Following surgery, the recovery rate of pituitary deficiency of one axis or more was 46% and the rate of new pituitary deficiency was 10%. Rates of LH-FSH, TSH, ACTH and GH deficiency recovery were 35.7%, 30.4%, 15.4%, and 45.5% respectively. Rates of new LH-FSH, TSH, ACTH and GH deficiencies were 8.3%, 1.6%, 9.2% and 5.1% respectively. Altogether, 24.6% of patients had a global pituitary function improvement and only 7% had pituitary function worsening after surgery. Male patients and patients with hyperprolactinemia upon diagnosis were more likely to experience pituitary function recovery. No prognostic factors for the risk of new deficiencies were identified. CONCLUSION: In a real-life cohort of patients with NFPAs, recovery of hypopituitarism after surgery is more frequent than the occurrence of new deficiencies. Hence, hypopituitarism could be considered a relative indication for surgery in patients with NFPAs.
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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 | 0.000 |
| 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) | 0.001 | 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".