Gender differences in stage at diagnosis and preoperative radiotherapy in patients with rectal cancer
Bibliographic record
Abstract
BACKGROUND: Few studies have examined gender differences in the clinical management of rectal cancer. We examine differences in stage at diagnosis and preoperative radiotherapy in rectal cancer patients. METHODS: A prospective cohort study was conducted in 22 hospitals in Spain including 770 patients undergoing surgery for rectal cancer. Study outcomes were disseminated disease at diagnosis and receiving preoperative radiotherapy. Age, comorbidity, referral from a screening program, diagnostic delay, distance from the anal verge, and tumor depth were considered as factors that might explain gender differences in these outcomes. RESULTS: Women were more likely to be diagnosed with disseminated disease among those referred from screening (odds ratio, confidence interval 95% (OR, CI = 7.2, 0.9-55.8) and among those with a diagnostic delay greater than 3 months (OR, CI = 5.1, 1.2-21.6). Women were less likely to receive preoperative radiotherapy if they were younger than 65 years of age (OR, CI = 0.6, 0.3-1.0) and if their tumors were cT3 or cT4 (OR, CI = 0.5, 0.4-0.7). CONCLUSIONS: The gender-specific sensitivity of rectal cancer screening tests, gender differences in referrals and clinical reasons for not prescribing preoperative radiotherapy in women should be further examined. If these gender differences are not clinically justifiable, their elimination might enhance survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".