Extended surgical resection for primary retroperitoneal sarcoma. Systematic review and meta- analysis
Bibliographic record
Abstract
Abstract Background & Objectives: Retroperitoneal sarcomas are often advanced at presentation. Surgery remains the only available curative management. The extent of surgical resection is debatable. There is a strong cause for compartmental resection of the whole hemi-retroperitoneum, but high-level evidence is lacking. This systematic review examines published evidence for the effect of resection policy on the oncologic outcome.Methods: The PubMed was searched for “retroperitoneal neoplasms”, “surgery”, “surgical procedures, operative”, and “margin of resection”. Web Of Science™ was searched for “retroperitoneal neoplasms” and “surgical management”. English-language articles that investigated retroperitoneal sarcoma in adult patients with extent of surgery as an independent variable and oncologic outcome as endpoints were included.Results: Twenty-three articles were retained for analysis. All articles were retrospective. Meta-analysis showed equivalence of overall survival with extended surgery versus limited surgery and with sole tumor resection versusen-blocresection with contiguous organs. Multivisceral resection did not increase morbidity.Conclusions: The role of universal extended surgery and the subset of patients who may benefit from irradiation treatment remain open questions.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".