Opting for conservative management over surgery for neonatal ovarian cysts
Bibliographic record
Abstract
OBJECTIVE: To explore the suitability of conservative management for neonatal ovarian cysts in newborns. METHODS: A retrospective cohort study was conducted, involving infants diagnosed with neonatal abdominal/pelvic cysts at two separate medical institutions from January 2015 through July 2021. Data collection included clinical characteristics, imaging results, pathological findings, and postnatal outcomes. Statistical analyses were performed using the Student's t-test, Mann-Whitney U-test, and receiver operating characteristic (ROC) curve. RESULTS: In total, 34 cases of neonatal abdominal/pelvic cystic masses were detected, with mean birth weight of 3401 ± 515 g. Of these, 22 patients underwent postnatal cystectomy/oophorectomy. Pathological assessments revealed 16 uncomplicated cysts, 5 complex cysts, and 1 ovarian cyst with torsion complications. Notably, the cysts' dimensions at the time of surgical intervention had significantly decreased from the initial measurements (p = 0.015). The ROC curve analysis presented an area under the curve of 0.642, indicating moderate accuracy in employing cyst size as a discriminative feature to differentiate complex from simple ovarian cysts. Additionally, a short-term follow-up of nonsurgical cases indicated a 100% resolution rate by 24 months of age (n = 9). CONCLUSION: Given their predominantly benign nature, the majority of neonatal ovarian cysts seem to be amenable to conservative management. This approach remains justified for larger cysts with minimal torsion risk, as well as considering the observed reduction in cyst size at birth, which further supports the case against surgical intervention.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".