Mucoceles of the appendix mimicking ovarian cysts
Bibliographic record
Abstract
Background: This review focuses on appendiceal mucoceles that have been reported to incidentally mimic ovarian cysts and put the patient at risk of misdiagnosis and, thus, inadequate treatment. Methodology: Review of the literature. Principal findings: Benign mucoceles are the most common form of appendiceal mucoceles and represent about 63-84%, whereas a malignancy is found in 11-20%. Patient presentation is extremely variable for all forms, with, first and foremost, nonspecific symptoms, and patients may also be completely asymptomatic. Thus, the majority of appendiceal mucoceles are discovered incidentally. Women with ovarian cysts also frequently report abdominal pain or nonspecific gastrointestinal symptoms, with about 10% of patients being asymptomatic. Gynecologists perform transvaginal ultrasound as a standard procedure to evaluate pelvic tumors from the uterus, tubes, or ovaries. The ultrasound appearance of appendiceal mucoceles can vary widely from well-encapsulated purely cystic lesions with anechoic fluid, hypoechoic masses with fine internal echoes, or complex hyperechoic masses. Thus, they can mimic ovarian cysts. However, a specific ultrasonographic marker is the so-called “onion skin sign” and its “dumbbell structure”. Even when an appendiceal mucocele is detected during gynecologic surgery, the appropriate surgical treatment would be an open approach, ideally combined with an intraoperative frozen section examination. The gynecologist might choose not to continue with the operation, instead performing the excision of the appendiceal mucocele as a two-step procedure. Conclusion/Significance: We advise gynecologists to consider the possibility of an appendiceal neoplasm, especially when a dumbbell structure in the lower right abdomen is found on ultrasound. If an appendiceal mucocele is incidentally diagnosed during surgery, a laparotomic approach is recommended if the operation is continued. This should be performed by a general surgeon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".