The Surgical Procedure in the Case of Ovarian Lesion in Neonate
Bibliographic record
Abstract
BACKGROUND: In newborns and infants, ovarian lesions can be detected during ultrasound examination before or after birth. Malignant ovarian lesions account for <1% of malignancies in newborns. However, in case of doubt about the nature of the lesion, surgery with tissue collection for histopathologic evaluation should be considered with the absolute condition of fertility preservation. OBSERVATIONS: The aim of this publication was to describe a case report of a 3-day-old infant who presented an ovarian lesion on postnatal ultrasound, with features suggesting a malignant nature of the ovary. In the described case, laparoscopy and mini-laparotomy were performed, torsion was excluded. The ovary was preserved, and histopathologic examination excluded the malignant nature of the lesion. CONCLUSION: A detailed analysis of the clinical status, laboratory tests, and imaging studies is necessary before making a final decision on further therapeutic, especially surgical management of a newborn with an ovarian lesion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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; 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".