Comparing epidemiological and clinical data from RPS patients documented in a German cancer registry to a cohort from TARPSWG reference centres
Bibliographic record
Abstract
Abstract Purpose Retroperitoneal sarcomas (RPS) are rare, heterogeneous tumours. Treatment recommendations are mainly derived from cohorts treated at reference centres. The applicability of data from cancer registries (CR) is controversial. This work compares CR and TARPSWG (Transatlantic Australasian Retroperitoneal Sarcoma Working Group) data to assess the representativeness of the TARPSWG and the applicability of the CR data. Methods TARPSWG cohort has previously been described. The CR Baden-Württemberg cohort includes patients with primary RPS M0 (years 2016–2021, ICD-10 C.49.4/5, C48.x) who underwent surgery within 12 months. Only patients with sarcoma-typical histology codes as used for the German Cancer Society certification system were included. Patient, tumour and therapy factors as well as survival times were compared with Chi 2 -test, Kaplan Meier curves, and adjusted models. Results 1000 (TARPSWG) and 364 (CR) patients were included. CR patients were older (median: 64 years vs. 58 years), had more high-grade tumours (FNCLCC 3 48.1% vs. 27.4%, p < 0.0001) and the 5-year survival rate was significantly lower (56.3% vs. 67.9%, p = 0.0015). The proportions of dedifferentiated liposarcoma (CR 37.1% vs. 37.0%) and leiomyosarcoma (CR 20.1% vs. 19.2%), and patterns of recurrence in these most frequent RPS subtypes were similar. Conclusion ICD-O/ICD 10 based filters appear to be a valid tool for extracting RPS cases from CR. The similar distribution and biological behavior of distinct RPS subtypes suggests that TARPS-WG are representative, and CR data may be used to verify recommendations derived from reference centre cohorts. Complementary use of data from different sources warrants further investigation in rare cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".