Understanding Characteristics, Treatment Patterns, and Clinical Outcomes for Individuals with Advanced or Recurrent Endometrial Cancer in Alberta, Canada: A Retrospective, Population-Based Cohort Study
Bibliographic record
Abstract
Endometrial cancer (EC) incidence has increased in recent decades. However, population-based outcomes data are limited. In this retrospective cohort study, we examined characteristics, treatment patterns, and clinical outcomes, including time to next treatment (TNNT) and overall survival (OS), among advanced/recurrent (A/R) EC patients between 2010 and 2018 in Alberta, Canada. Kaplan–Meier statistics evaluated TTNT and OS, stratified by patient (A/R) and treatment. A total of 1053 patients were included: 620 (58.9%) advanced and 433 (41.1%) recurrent. A total of 713 (67.7%) patients received first-line therapy: 466 (75.2%) advanced and 247 (57.0%) recurrent. Platinum-based chemotherapy (PBCT) was the most common first-line regimen (overall: 78.6%; advanced: 96.1%; recurrent: 45.3%). The median TTNT and OS from first-line therapy were 19.9 months (95% confidence interval [CI]: 17.5–23.5) and 35.9 months (95% CI: 31.5–53.5), respectively. Following first-line PBCT, the median OS from second-line chemotherapy (N = 187) was 10.4 months (95% CI: 8.9–13.3) and higher for those rechallenged with PBCT (N = 72; 38.5%) versus no rechallenge (N = 115; 61.5%) (13.3 months [95% CI: 11.2–20.9] vs. 6.4 months [95% CI: 4.6–10.4; p < 0.001]). The findings highlight poor outcomes in A/R EC, particularly following first-line therapy, and that additional tolerable therapeutic options are needed to improve patient outcomes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".