Urothelial cancer: Impact of gender on stage at diagnosis and survival.
Bibliographic record
Abstract
316 Background: Gender differences for disease course and survival in various cancers exist. Gender disparity for both stage at diagnosis and overall survival (OS) has been observed in urothelial cancer (UC). We report a single institution analysis of UC patients treated with chemotherapy to further investigate gender differences in outcomes. Methods: We identified 198 bladder cancer pts treated with chemotherapy since 2002. Chemotherapy was either given as adjuvant or palliative and the most common regimens used were gemcitabine and cisplatin, gemcitabine and carboplatin or gemcitabine alone. Age and stage at diagnosis, sex, smoking status, radiation exposures, bloodwork as a measure of organ dysfunction and overall survival info was collected. Outcomes were compared using Chi Square Statistic. Results: Age at diagnosis, smoking status and prior pelvic radiation were not significantly different (females 66.1 yrs vs males 63.6 yrs; 54% smokers in both groups; 8.3% females vs 7.6% males exposed to radiation). Significantly more females were diagnosed with advanced disease than men (70.8% vs 58.7%, p=0.049) vs earlier stages (stage 0-I) (12.2% vs 35.9%, p=0.03). For patients deceased, OS was not significantly different between genders when analysed for all stages combined (deceased 41.5 vs 39.9 mos), or for those diagnosed only at Stage IV (deceased 12.4 vs 8.6 mos). Of patients still alive at time of review, a survival advantage was apparent for men at all stages (54.8 vs 38.7 months), as well as with stage IV disease (35.9 vs 19.7 months). Gemcitabine-cisplatin was given more often to men with stage IV disease than females (93% vs 63%, p<0.02) despite no difference in organ dysfunction, or ECOG performance status in females. Conclusions: We observed that while both genders are similar with respect to age at UC diagnosis, risk factors exposures (smoking, radiation) and pathological variants, females were diagnosed at later stages, and receive standard first line therapy less often. Our data suggest that this impacts negatively on OS in females diagnosed in earlier disease stages. Further research is needed to identify if we can improve outcome by promoting earlier diagnosis and more aggressive management in earlier disease in females.
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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 | 0.000 | 0.001 |
| 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) | 0.004 | 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".