Data Dissemination of the Role of Neoadjuvant Radiation in Retroperitoneal Sarcoma: A CTOS and CSSO Survey
Bibliographic record
Abstract
Consensus guidelines call for complete resection of retroperitoneal sarcoma with consideration of neoadjuvant radiation for curative-intent treatment. The 15-month delay from the initial presentation of an abstract to the final publication of the STRASS trial results assessing the impact of neoadjuvant radiation led to a dilemma of how patients should be managed in the interim. This study aims to (1) understand perspectives regarding neoadjuvant radiation for RPS during this period; and (2) assess the process of integrating data into practice. A survey was distributed to international organizations including all specialties treating RPS. Eighty clinicians responded, including surgical (60.5%), radiation (21.0%) and medical oncologists (18.5%). Low kappa correlation coefficients on a series of clinical scenarios querying individual recommendations before and after initial presentation as an abstract indicate considerable change. Over 62% of respondents identified a practice change; however, most also noted discomfort in adopting changes without a manuscript available. Of the 45 respondents indicating discomfort with practice changes without a full manuscript, 28 (62%) indicated that their practice changed in response to the abstract. There was substantial variability in recommendations for neoadjuvant radiation between the presentation of the abstract and the publication of trial results. The difference in the proportion of clinicians describing comfort with changing practice based on the presentation of the abstract versus those that had done so shows that indications for proper integration of data into practice are not clear. Endeavors to resolve this ambiguity and expedite availability of practice-changing data are warranted.
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 | 0.088 | 0.240 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".