Serum Survivin Increases in Prolactinoma
Bibliographic record
Abstract
BACKGROUND: Prolactinoma is the most common adult pituitary adenoma. Survivin is a member of the family of inhibitors of apoptosis proteins. Its expression is observed in many tumors. Survivin expression has shown in prolactinoma tissue before but no study exists showing serum survivin level. The aim of the present study was to investigate serum survivin levels in patients with prolactinoma and demonstrate its value in diagnosis of the disease. METHODS: The group of patients consisted of 25 women, aged from 17 to 51 years. As a control group, 21 healthy women, aged from 22 to 45 years were included. Twenty patients had microprolactinoma, while five patients had macroprolactinoma. All patients had received dopamine agonist treatment. Serum survivin levels were measured in all of the groups. RESULTS: Survivin levels were significantly higher in prolactinoma patients compared to controls (19.04 (10 - 38) pg/mL; 15.05 (8 - 22) pg/mL; P = 0.042). There was no difference between microadenoma and macroadenoma patients in survivin levels (19.22 (10 - 38) pg/mL; 18.40 (16 - 22) pg/mL; P = 0.914). In correlation analysis, survivin was not correlated with other parameters. CONCLUSIONS: We consider that higher survivin levels might be a molecular marker predicting the presence of prolactinoma and may be useful for the diagnosis. But large-scale research is needed to clarify its role in diagnosis of prolactinoma patients.
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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 | 0.001 |
| 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 | 0.000 |
| 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".