Analysis of Risk Factors, Treatment Patterns, and Survival Outcomes After Emergency Presentation With Colorectal Cancer: A Prospective Multicenter Cohort Study in Nigeria
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Prospective data on presentation and outcomes of colorectal cancer (CRC) in Nigeria are limited; however, emergency presentation with advanced disease is thought common. METHODS: Consecutive CRC patients presenting at six sites over 6 years were included. Risk factors for emergency presentation were evaluated using logistic regression methods. Overall survival (OS) was compared between emergent and elective patients using Kaplan-Meier methods and the log-rank test. RESULTS: Of 535 patients, 30.7% presented emergently. Median age was 56 years, 55% were men, and 5.0% reported a cancer family history. Emergency patients had more proximal cancers (42.1% vs. 24.0%), Stage IV disease (61.6% vs. 40.2%; p < 0.001), lower household income (₦35 000/month vs. ₦50 000/month), lower education levels (p = 0.008) and accessed care with nonmotorized transport (50.6% vs. 37.2%; p = 0.005). Median OS was shorter in the emergency group (6.4 vs. 17.4 months; p < 0.001). Across clinical stages, emergency presentation was associated with worse OS (Stage IV median OS 4.8 vs. 9.4 months; p = 0.002). Surgery improved survival in both groups, although emergency patients had higher 30-day postoperative mortality (23.2% vs. 9.1%; p < 0.001). CONCLUSIONS: Emergent Nigerian CRC patients have worse OS than elective patients. Cancer control efforts should focus on faster cancer detection, early presentation, diagnosis, and treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".