Pathological brain lesions in girls with central precocious puberty at initial diagnosis in Southern Vietnam
Bibliographic record
Abstract
PURPOSE: Cranial magnetic resonance imaging (MRI) is recommended to identify intracranial lesions in girls with central precocious puberty (CPP). Yet, the use of routine MRI scans in girls with CPP is still debatable, as pathological findings in girls 6 years of age or older with CPP are limited. Therefore, we aimed to identify the prevalence of brain lessons in CPP patients stratified by age group (0-2, 2-6, and 6-8 years). METHODS: This retrospective cross-sectional study recruited 257 girls diagnosed with CPP for 6 years (2010-2016). MRI was used to detect brain abnormalities. Levels of luteinizing hormone, follicle-stimulating hormone, and sex hormones in blood samples were measured. RESULTS: Most girls had no brain lesions (82.9%, n=213), and of the minor proportion of girls with CPP that exhibited brain lesions (17.1%, n=44), 32 girls had organic CPP. Pathological findings were detected in 33.3% (2 of 6) of girls aged 0-2 years, 15.6% (5 of 32) of girls aged 2-6 years, and 3.6% (8 of 219) of girls aged 6-8 years. Hypothalamic hamartoma and tumors in the pituitary stalk were the most common pathological findings. The likelihood of brain lesions decreased with age. Girls with organic CPP were more likely to be younger (6.1±2.4 vs. 7.3±1.3 years, p<0.01) than girls with idiopathic CPP. CONCLUSION: Older girls appeared to have a lower prevalence of organic CPP. Clinicians should cautiously use cranial MRI for girls aged 6-8 years with CPP.
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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 | 0.000 |
| 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) | 0.002 | 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".