Retroperitoneal pelvic tumours in women: diagnostic and therapeutic challenges.
Bibliographic record
Abstract
BACKGROUND: Gynaecologic pelvic tumours are very common and they can present with a variety of symptoms depending on their size, location, pathophysiology and histogenesis. Infrequently, some pelvic tumours are found in the retroperitoneal space presenting with similar symptoms. Our objective is to present our experience and review of pertinent literature on miscellaneous retroperitoneal tumours. METHODS: Four women with retroperitoneal tumours (one schwannoma, one granulosa cell tumour and two hindgut (tail gut) cysts)) were encountered during routine laparoscopy (3 cases) and laparotomy (one case). Following multidisciplinary consultation and additional imaging, all tumours were removed by laparotomy with one case provoking litigation due to ureteral and bowel injury. RESULTS: Using these four cases, and additional cases from the literature, we highlight the potential pitfalls and provide an algorithm to minimize risks and adverse clinical and legal outcomes associated with unexpected retroperitoneal tumours. The algorithm includes resisting the impulse/temptation to remove or biopsy these tumours, requesting intra-operative consultation(s), obtaining additional detailed imaging to characterize these tumours, providing appropriate counselling to patients, obtaining informed consent, and consulting the appropriate surgical teams. At times, an interdisciplinary approach may prove to be the best course of action in order to optimize treatment and ensure patient safety. CONCLUSION: If a retroperitoneal tumour is unexpectedly encountered, it is imperative to have intra-operative consultation (if available), to not attempt excision or biopsy, and to subsequently obtain post-operative multidisciplinary consultations, specific imaging, and information gathering in order to treat these heterogeneous masses as safely as possible.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".