The effect of married status on cancer‐specific mortality in nonmetastatic pelvic liposarcoma patients according to sex
Bibliographic record
Abstract
BACKGROUND: In nonmetastatic pelvic liposarcoma patients, it is unknown whether married status is associated with better cancer-control outcome defined as cancer-specific mortality (CSM). We addressed this knowledge gap and hypothesized that married status is associated with lower CSM rates in both male and female patients. METHODS: Within the Surveillance, Epidemiology, and End Results database (2000-2020), nonmetastatic pelvic liposarcoma patients were identified. Kaplan-Meier plots and univariable and multivariable Cox regression models (CRMs) predicting CSM according to marital status were used in the overall cohort and in male and female subgroups. RESULTS: Of 1078 liposarcoma patients, 764 (71%) were male and 314 (29%) female. Of 764 male patients, 542 (71%) were married. Conversely, of 314 female patients, 192 (61%) were married. In the overall cohort, 5-year cancer-specific mortality-free survival (CSM-FS) rates were 89% for married versus 83% for unmarried patients (Δ = 6%). In multivariable CRMs, married status did not independently predict lower CSM (hazard ratio [HR]: 0.74, p = 0.06). In males, 5-year CSM-FS rates were 89% for married versus 86% for unmarried patients (Δ = 3%). In multivariable CRMs, married status did not independently predict lower CSM (HR: 0.85, p = 0.4). In females, 5-year CSM-FS rates were 88% for married versus 79% for unmarried patients (Δ = 9%). In multivariable CRMs, married status independently predicted lower CSM (HR: 0.58, p = 0.03). CONCLUSIONS: In nonmetastatic pelvic liposarcoma patients, married status independently predicted lower CSM only in female patients. In consequence, unmarried female patients should ideally require more assistance and more frequent follow-up than their married counterparts.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".