Customized early detection of colorectal cancer in Nigeria identifies advanced adenomas and early‐stage disease
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) incidence is increasing in low- and middle-income countries, where late-stage presentation is common and survival rates remain poor. Early-detection programs are critical to improving outcomes. METHODS: A longitudinal early-detection study was conducted in Osun State, Nigeria. A 6-month community awareness campaign was implemented with posters, radio jingles, social media, and messaging disseminated via health and religious institutions. CRC knowledge was assessed before and after the intervention with the validated Bowel Cancer Awareness Measure questionnaire. Individuals with indicators of CRC were referred from peripheral facilities to an early-diagnosis (ED) clinic at a tertiary center. Demographic data, presenting features, and diagnostic outcomes were prospectively recorded. The primary end point was detection of advanced adenomas and CRC. RESULTS: Of 497 eligible participants, 322 (64.8%) completed pre- and postcampaign surveys. Awareness of CRC improved from 54 (16.8%) to 311 (96.9%) (p < .001). Good knowledge of CRC risk factors and symptoms also increased significantly (p < .001). A total of 329 individuals were navigated to the ED clinic; 168 (51.1%) were eligible for the protocol, and 116 (73.0%) completed colonoscopy. CRC was diagnosed in four patients (3.4%), with stage 0 (n = 2), II (n = 1), and III (n = 1). Advanced adenomas were identified in 11% of patients (13 of 116) who underwent colonoscopy. CONCLUSIONS: Combining community engagement with patient navigation significantly increased CRC awareness and enabled the detection of advanced adenomas and early-stage cancers. Expanding this model to a national level is recommended to evaluate broader impact, cost-effectiveness, and potential implementation challenges.
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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 | 0.002 |
| 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) | 0.001 | 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".