Bibliographic record
Abstract
BACKGROUND: No population-based studies of retroperitoneal sarcoma (RPS) have been conducted, and the use and timing of adjuvant radiotherapy for RPS is controversial. The objective of this study was to examine the incidence and treatment of RPS, specifically regarding the use of adjuvant radiotherapy. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was used to evaluate the incidence of RPS over a 29-year period (1973-2001). The rate of surgery, the rate and timing of adjuvant radiotherapy, and the influence of demographic factors on treatment were evaluated. RESULTS: A total of 2348 cases of RPS were identified. The mean annual incidence of RPS was 2.7 cases per 10(6) persons and did not change significantly over time (2.6 in 1973 vs. 2.8 in 2001; P = .92). Most patients (1654; 70.4%) underwent surgical resection. Radiotherapy was used in 428 patients (25.9%) who underwent surgery; radiation was given postoperatively in 366 (85.5%), preoperatively in 20 (4.7%), and intraoperatively or unknown in 42 (9.8%). Patients who received any adjuvant radiotherapy were on average 5 years younger than those who underwent surgery alone (P < .0001). Radiotherapy was more commonly used among whites than African Americans (25.8% vs. 16.7%; P = .02) and there was significant variation in the use of adjuvant radiotherapy by geographic location (P = .003). On multivariate analysis, race (P = 0.004), age (P < .0001), and geographic location (P = .006) were independently associated with the use of adjuvant radiotherapy. CONCLUSION: The incidence of RPS, a rare disease, appears stable. Most patients who undergo surgery do not receive any adjuvant radiotherapy, and very few receive preoperative radiotherapy. Differences in adjuvant radiotherapy use related to demographic and geographic factors suggest that at least some treatment variations reflect differences in individual and institutional practice patterns.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".