Hypersecretion of the α-subunit in clinically non-functioning pituitary adenomas: Diagnostic accuracy is improved by adding α-subunit/gonadotropin ratio to levels of α-subunit
Bibliographic record
Abstract
BACKGROUND: In vitro, the majority of clinically non-functioning pituitary adenomas (NFPAs) produce gonadotropins or their alpha-subunit; however, in vivo, measurements of alpha-subunit levels may not accurately detect the hypersecretion of the alpha-subunit. AIM: We wanted to estimate the reference intervals and decision limits for gonadotropin alpha-subunit, LH and FSH levels, and aratio (alpha-subunit/LH+FSH), especially taking into consideration patient gender and menstrual status. Furthermore, we wanted to examine if the diagnostic utility of alpha-subunit hypersecretion was improved when the alpha-ratios, rather than simply the alpha-subunit levels, were measured in patients with NFPAs. MATERIAL AND METHODS: Reference intervals for gonadotropin alpha-subunit serum levels and alpha-ratios were established in 231 healthy adults. The estimated cut-off limits were applied to 37 patients with NFPAs. Gonadotropin alpha-subunit, LH and FSH levels were measured and alpha-ratios were calculated. RESULTS: In healthy adults, the cut-offs for alpha-subunit levels were significantly different between men and pre- and postmenopausal women: the cut-offs were 1.10, 0.48 and 3.76 IU/l, respectively. Using these estimated cut-offs, increased alpha-subunit levels were identified in 10 out of 37 (27%) patients with NFPAs. By adding alpha-ratio, in combination with alpha-subunit levels, 23 patients out of 37 (62%) were identified as having elevated alpha-subunit hypersecretion, and 22 out of these 23 patients (96%) had increased alpha-ratios. One premenopausal patient out of 23 had elevated alpha-subunit level but a normal alpha-ratio. CONCLUSION: Our data suggest that adding the simple calculation of alpha-ratio improves the ability of detecting gonadotropin alpha-subunit hypersecretion and thereby indentifying 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 | 0.002 | 0.008 |
| 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 | 0.001 | 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".