Bibliographic record
Abstract
Abstract Significant advances have been made over the past decade in the understanding of clinicopathologic prognostic factors for soft tissue sarcoma. Foremost among these advances is an improved ability to recognize the subset of patients at high risk for recurrent disease and tumor‐related death based on clinicopathologic data available at the time of initial presentation. Recent advances have also helped to elucidate specific molecular factors that have independent prognostic significance. This review summarizes the available data on traditional clinicopathologic, medical, and molecular prognostic factors for adult soft tissue sarcoma. Expected outcomes will be provided, drawing in particular from the results of a large series of patients managed at one center. Although there are many potential outcomes that could be assessed, the discussion will focus almost entirely on the traditional oncology outcomes of local control, metastatic risk, and survival. Other important outcomes, such as limb preservation; function, and quality of life will not be emphasized because of space limitations. The factors will be discussed in a framework that focuses on thetumor‐related,host‐related(or patient associated), andenvironment‐relatedbackground for these factors. The review will conclude with a summary tabulation identifying strata of the importance of factors to everyday clinical practice using the principles outlined in Chapter 2. These includeessentialfactors (needed for treatment decision‐making),additionalfactors (of benefit for describing a cohort of patients), andnew and promisingfactors that may of benefit in the future to exploit new treatment or diagnostic strategies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".