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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".