Epidemiology, Treatment Patterns, Survival, Healthcare Resource Utilization, and Costs of Dedifferentiated Liposarcoma (DDLPS) in Canada: A Retrospective Cohort Study Using Administrative Databases in Ontario
Bibliographic record
Abstract
Background: Dedifferentiated liposarcoma (DDLPS) is a rare, aggressive tumour with poor survival outcomes in advanced settings. This study assessed the incidence/prevalence, treatment patterns, survival, healthcare resource utilization (HCRU), and costs for DDLPS patients in Ontario, Canada. Methods: A retrospective cohort study was conducted among DDLPS patients between 2010 and 2022 using administrative databases. Overall survival, all-cause HCRU, and costs (2023 Canadian dollars, CAD) were compared based on advanced disease and resection status. Results: The overall incidence and cumulative prevalence of DDLPS was 0.465 and 1.995 per 100,000 people, respectively. Of all 611 DDLPS cases (64.3% male, median age [IQR]: 67 [57–76] years), 40.3% and 61.0% had advanced and unresected disease, respectively. The median overall survival (mOS) was 69 months [IQR = 15–151] for the entire cohort, but this was significantly lower for advanced and unresected disease (p < 0.0001). Among patients receiving systemic treatments (N = 117), 81.2% were prescribed doxorubicin as first-line treatment. All-cause healthcare costs (2023 CAD) amounted to CAD 34,448 per person-year (PPY), with inpatient hospitalizations being the highest cost driver at CAD 14,522 PPY and 0.8 inpatient hospitalization PPY for all years. Advanced disease had higher HCRU and costs. Conclusions: This is the first comprehensive real-world evidence study that quantifies the high mortality and cost burden associated with DDLPS in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".