Clinical, Endocrine and Neuroimaging Findings in Girls With Central Precocious Puberty
Bibliographic record
Abstract
CONTEXT: The etiology of central precocious puberty (CPP) includes a spectrum of conditions. Girls younger than age 6 years with CPP should undergo cranial magnetic resonance imaging (MRI), but it remains controversial whether all girls who develop CPP between the ages of 6 and 8 years require neuroimaging examination. OBJECTIVE: To investigate the frequency of brain MRI abnormalities in girls diagnosed with CPP and the relationship between maternal factors, their age at presentation, clinical signs and symptoms, hormonal profiles, and neuroimaging findings. METHODS: Data were collected between January 2005 and September 2019 from 112 girls who showed clinical pubertal progression before 8 years of age who underwent brain MRI. RESULTS: MRI was normal in 47 (42%) idiopathic (I) scans, 54 (48%) patients had hypothalamic-pituitary anomalies (HPA) and/or extra-HP anomalies (EHPA), and 11 (10%) had brain tumors or tumor-like conditions (BT/TL), including 3 with neurological signs. Associated preexisting disorders were documented in 16. Girls with BT/TL had a higher LH peak after GnRH test (P = 0.01) than I, and those older than age 6 years had a higher craniocaudal diameter of the pituitary gland (P = 0.01); their baseline FSH and LH (P = 0.004) and peak FSH (P = 0.01) and LH (P = 0.05) values were higher than I. Logistic regression showed maternal age at menarche (P = 0.02) and peak FSH (P = 0.02) as BT/TL risk factors. CONCLUSIONS: MRI provides valuable information in girls with CPP by demonstrating that fewer than half have a normal brain MRI and that few can have significant intracranial lesions after the age of 6, despite the absence of suggestive neurological signs.
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 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 | 0.003 |
| 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) | 0.002 | 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".