Retroperitoneal sarcomas - A retrospective study
Bibliographic record
Abstract
Outcomes of the management of retroperitoneal sarcoma in the Northern Alberta (NA), Canada was examined in this study. 75 patients (34 females and 41 males, age range 30-87 years) underwent operations at the CCI for PRS (sarcomas represented 61% of all the cases). A retrospective analysis of these patients was performed to determine the prognostic parameters associated with a favorable prognosis. Complete resection was possible in 33% of cases (n = 25), incomplete resection was performed in 35% (n = 26) and in 32% (n = 24) only biopsy was possible. Frequently resected adjacent organs were: kidney (15%), colon (10%) and pancreas (6%). Univariate analysis demonstrated that complete resection was an independent factor for survival as compared to partial resection or biopsy alone (p=0.001). Patients with complete resection had a 12 month survival of 100% (n = 25) compared to 84% (n = 22) for those undergoing partial resection and 25% (n = 5) for those with simple biopsy. A 24-month survival of the patients undergoing complete resection was 88% (n = 22). Median survival for type of surgical treatment was 91.2 (88.2-104.9) months for complete resection compared to 30.4 (24.2-41.5) months for partial resection and only 5.7 (2.6-8.1) months for biopsy. Complete resection is the cornerstone of the treatment and is important for long-term survival in patients with retroperitoneal soft tissue sarcomas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".