What Should Be the Topics of a Prospective Study on Ovarian Masses in Children?—Results of a Multicenter Retrospective Study and a Scoping Literature Review
Bibliographic record
Abstract
Purpose: to determine management problems of ovarian masses in girls in order to form a baseline for prospective randomized studies of the established topics and quality improvement of our management. Materials and Methods: We performed a national analysis of clinical aspects of ovarian masses in girls operated on in Poland, analyzed retrospectively medical files of all consecutive patients aged 0–18 who underwent surgeries for ovarian lesions between 2012 and 2017 at 17 pediatric surgical departments and complemented the analysis with a scoping review of a recent primary research related to ovarian masses in children. Results: The study group comprised 595 patients. Forty-four (7.39%) girls were diagnosed with malignant tumors. The overall preservation rate was 64.54%. The analysis revealed that positive tumor markers (OR = 10.3), lesions larger than 6 cm (OR = 4.17) and solid mass on ultrasound examination (OR = 5.34) are interdependent variables differentiating malignant tumors from non-malignant lesions (X42 = 79.1; p = 0.00000). Our scoping review revealed 10 major branches of research within the topic of ovarian masses in pediatric population. Conclusions: We have developed an overview of the field with the emphasis on the local environment. Our next step is a multi-institutional prospective study of a quality improvement project implementation based on the obtained knowledge.
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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 | 0.033 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".