Development of a surgical complexity score to predict postoperative morbidity following primary retroperitoneal sarcoma resection: a collaborative study from the Transatlantic Australasian Retroperitoneal Sarcoma Working Group (TARPSWG)
Bibliographic record
Abstract
Soft tissue sarcomas (STS) are a rare group of neoplasms that demonstrate diverse tumour biology influenced by both histology and anatomic location. Approximately 15–20% of STS arise in the retroperitoneum1,2. In the absence of effective (neo)adjuvant therapies, a macroscopically complete (R0/R1) resection remains the cornerstone of treatment for patients with localized primary retroperitoneal sarcomas (pRPS). These tumours are commonly large and often abut/involve multiple adjacent viscera. The extent of resection is dictated by anatomical considerations and frequently requires multivisceral resection en bloc with the tumour3,4. Histologic type also informs the extent of resection, based on predilection for histopathologic organ invasion as well as underlying inherent tumour biology that predicts the risk of local and/or distant recurrence5–7. Despite optimal surgery, recurrence rates are high depending on the histology8–13. The aggressiveness of surgery has been somewhat tempered by the potential morbidity rate associated with multivisceral resection14. Surgical risk assessment is a critical step when planning any operation. Assessment of perioperative morbidity rate in patients undergoing resection for pRPS has been challenging to quantify. A tool frequently used to assess perioperative morbidity and mortality rates is the American College of Surgeons National Surgical Quality Improvement Program's (ACS NSQIP) surgical risk calculator (http://riskcalculator.facs.org)15. This incorporates a current procedural terminology (CPT) code and 19 preoperative patient variables to predict probability of 12 different postoperative outcomes including major complications, reoperation, and death. The patient characteristics included in the calculator are variables that are known prior to surgery such that the anticipated operation can be used to assess preoperative surgical risk as well as guide the informed consent process. One of the shortcomings of this calculator for discussions with patients planned for resection of pRPS is that there is significant variability in the extent and complexity of operations that may be described by a single CPT code corresponding to the resection of a retroperitoneal mass.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".