P.098 Evaluation and surgical management of pelvic peripheral nerve sheath tumors: the University of Toronto experience and review of literature
Bibliographic record
Abstract
Background: Pelvic peripheral nerve sheath tumors (PNST), which includes neurofibroma, schwannoma, and MPNST, are rare tumors located in the retroperitoneum. Methods: The case records of a prospectively maintained database at Sunnybrook Health Sciences Center (SHSC) were reviewed to identify patients with pelvic PNST, managed between 2006 - 2016. Medical records were retrospectively reviewed for patient demographics, presentation, tumor location, symptoms, imaging characteristics, management, and outcome. The surgical technical caveats were described. An English language literature review was performed to describe previously published experiences. Results: The series consisted of 7 patients, ranging from 22 - 74 years of age at presentation. These lesions tend to be large at the time of diagnosis, and presenting symptoms include abdominal, flank, or back pain, as well as leg edema or hydronephrosis from local compression. Most patients in this cohort were managed surgically with midline abdominal transperitoneal exposures. Lastly, 5 tumors were benign schwannomas managed with gross total resection or debulking, while 2 patients had MPNSTs managed with biopsy followed by adjuvant chemoradiation therapy. Conclusions: In this case series, we describe the characteristics, evaluation, and management of 7 patients with pelvic PNST at a major healthcare institution in Toronto, Canada, highlighting the technical aspects of managing this rare and challenging entity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".