Abstract 111: Colorectal Cancer Presentation and Survival Outcomes in Nigeria: A Prospective Multi-Centre Cohort Study of 543 Patients
Bibliographic record
Abstract
Abstract Purpose: Prospective data on colorectal cancer (CRC) presentation and outcomes in Nigeria is limited, however emergency presentation with advanced disease is thought to be common. Using the African Research Group for Oncology's prospective, multisite database of CRC patients in Southwest Nigeria, we evaluated risk factors for emergency CRC presentation and effects on overall survival (OS). Methods: Consecutive CRC patients presenting between 2013 and May 2020 to five referral sites were included. Demographic, socioeconomic and disease-related risk factors for emergency presentation were evaluated using univariate and multivariable logistic regression methods. We compared OS between emergency and elective patients, stratified for clinical stage, using Kaplan-Meier survival and Cox proportional hazards methods. Results: Of 543 CRC patients, 168 (30.9%) presented emergently. Median age at diagnosis was 55 years, 53% were male, and 4.8% reported a family history of cancer. Patients presenting emergently were more likely to have cancers proximal to the splenic flexure (41.1% vs 24.6%, p<0.001), stage IV disease (62.7% vs 43.4%, p<0.001) use non-motorized transport (54.4% vs 37.7%, p=0.001), have a lower median household income (USD$65/month vs $USD79/month, p<0.04), and lower education level than those presenting electively. On multivariable regression analysis, only low BMI, stage IV disease, mode of transport and hospital site were significantly associated with emergency presentation. Median OS was 5.4 months (IQR 0.9-14.5; n=146) in the emergency group compared to 10.7 months (IQR 3/8-22.5; n=375) among elective patients. After adjustment for clinical stage at diagnosis, emergency presentation was still associated with significantly worse OS (HR 1.52; 95% C.I. 1.36-1.63, p<0.001). Conclusion: A high proportion of CRC patients in Nigeria present emergently, with advanced disease. Even after adjusting for clinical stage, these patients have worse overall survival. Earlier detection of cancers, including removing barriers to timely presentation and diagnosis, should be a focus of cancer control efforts. Citation Format: Adeniyi Aderibigbe, Anna Dare, Gregory Knapp, Olusegan Alatise, T. Peter Kingham. Colorectal Cancer Presentation and Survival Outcomes in Nigeria: A Prospective Multi-Centre Cohort Study of 543 Patients [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 111.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".