Update on Current Evidence for the Diagnosis and Management of Nonfunctioning Pituitary Neuroendocrine Tumors
Bibliographic record
Abstract
Pituitary neuroendocrine tumors (PitNETs) are the third most frequently diagnosed intracranial tumors, with nonfunctioning PitNETs (nfPitNETs) accounting for 30% of all pituitary tumors and representing the most common type of macroPitNETs. NfPitNETs are usually benign tumors with no evidence of hormone oversecretion except for hyperprolactinemia secondary to pituitary stalk compression. Due to this, they do not typically present with clinical syndromes like acromegaly, Cushing's disease or hyperthyroidism and instead are identified incidentally on imaging or from symptoms of mass effects (headache, vision changes, apoplexy). With the lack of effective medical interventions, first-line treatment is transsphenoidal surgical resection, however, nfPitNETs often have supra- or parasellar extension, and total resection of the tumor is often not possible, resulting in residual tumor regrowth or reoccurrence. While functional PitNETs can be easily followed for recurrence using hormonal biomarkers, there is no similar parameter to predict recurrence in nfPitNETs, hence delaying early recognition and timely management. Therefore, there is a need to identify prognostic biomarkers that can be used for patient surveillance and as therapeutic targets. This review focuses on summarizing the current evidence on nfPitNETs, with a special focus on potential new biomarkers and therapeutics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".