Epidemiology and survival outcomes for liposarcoma patients in England: An observational cohort study using real world data
Bibliographic record
Abstract
Purpose: This real-world data study evaluates demographic and clinical characteristics and survival in patients with liposarcoma to inform our understanding of treatment patterns and associated outcomes in this rare cancer. Materials and methods: A retrospective cohort study was conducted using existing data available through the Clinical Practice Research Datalink. Male and female patients aged 18 years or older who had their first ever record of liposarcoma between 1998 and 2018 were included in the study. The demographic and tumour characteristics were presented (overall, by first line treatment and for dedifferentiated liposarcoma only) as proportions (percentages) while continuous variables were presented as means with standard deviation and interquartile ranges. Survival rates from diagnosis date and first line treatment (with 95% confidence intervals) were also calculated. Results: 1,315 patients were included, of which 46% (611) had a treatment recorded. Most patients were male and over the age of 60 years. Surgery was the most frequent treatment received following diagnosis (34% of all patients), followed by radiotherapy (8%) and chemotherapy (2.4%) with the remaining patients having no record of treatment available. Overall, there was a 77% probability of survival after diagnosis at 5 years. Conclusions: Findings from this study help advance our understanding of real world patient characteristics, treatment patterns and survival outcomes in a rare and heterogeneous cancer, which may be useful for guiding clinical management. This study also identified challenges with using real world data, which can be minimised through improving data collection and standardisation.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".