The effect of the setting of a positive surgical margin in soft tissue sarcoma
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to evaluate the risk of local recurrence and survival after soft tissue sarcoma (STS) resection with positive margins and to evaluate the safety of sparing adjacent critical structures. METHODS: One hundred sixty-nine patients with localized STS who had positive resection margins were identified from a prospective database. Patients who had positive margins were stratified into 3 groups, each representing a specific clinical scenario: critical structure positive margin (eg major nerve, vessel, or bone), tumor bed resection positive margin, and unexpected positive margin. The rates of local recurrence-free survival (LRFS) and cause-specific survival (CSS) were calculated and compared with relevant control patients who had negative margins after STS resection. RESULTS: After planned close dissection to preserve critical structures, the 5-year LRFS and CSS rates both depended on the quality of the surgical margins (97% and 80.3%, respectively, for those with negative margins vs 85.4% and 59.4%, respectively, for those with positive margins; P = .015 and P = .05, respectively). Negative margins achieved through resection of critical structures because of tumor invasion or encasement only slightly improved the 5-year rates of LRFS (91.2%) and CSS (63.6%; P = .8 and P = .9, respectively). The lowest 5-year LRFS and CSS rates were 63.4% and 59.2%, respectively, after an unexpected positive margin during primary surgery. CONCLUSIONS: After patients undergo resection of STS with positive margins, oncologic outcomes can be predicted based on the clinical context. Sparing adjacent critical structures in this setting is safe and contributes to improved functional outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".