Hormônio folículo estimulante como preditor do achado de espermatozóides móveis na biópsia testicular de casos de azoospermia
Bibliographic record
Abstract
OBJECTIVE: To define predictive factors of mobile spermatozoa recovery in azoospermic patients. METHODS: Testicular volume, serum follicle stimulating hormone (FSH), luteinizing hormone (LH), prolactin (PRL) and testosterone levels were assessed in 60 azoospermic patients. Patients underwent bilateral testicular biopsy with local anesthesia. Samples were classified according to absence of spermatozoa, presence of motile and nonmotile spermatozoa, and histological findings. Age, hormone levels, testicular volume and histology with motile spermatozoa recovery were compared. P < 0.05 was considered significant. RESULTS: Non-obstructive azoospermia was diagnosed in 45 patients. Significant differences were detected between the group with motile spermatozoa recovery and the group with absence of spermatozoa in terms of FSH levels (P=0.037 ANOVA one-way). A ROC curve was used to define FSH values below 16.05 IU/L (sensitivity: 76.2%, specificity: 67.7%) as predictive factors for motile spermatozoa recovery. Other statistical differences were not detected. CONCLUSIONS: FSH levels below 16.05 IU/L showed good accuracy to predict the presence of motile spermatozoa in the testicular biopsy of azoospermic patients. Physical examination, testosterone levels, LH and prolactin were not useful as predictive factors in the present study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".