Delays in Presentation, Diagnosis, and Treatment Among Patients With GI Cancer in Southwest Nigeria
Bibliographic record
Abstract
PURPOSE The incidence of GI cancers is increasing in sub-Saharan African countries. We described the oncological care pathway and assessed presentation, diagnosis, and treatment intervals and delays among patients with GI cancer who presented to the Obafemi Awolowo University Teaching Hospitals Complex in Ile-Ife, Nigeria. METHODS We analyzed data from 545 patients with GI cancer in the African Research Group for Oncology (ARGO) database. We defined presentation interval as the interval between symptom onset and presentation to tertiary hospital, diagnostic interval as between presentation and diagnosis, and treatment interval as between diagnosis and initiation of treatment. We considered >3 months, >1 month, and >1 month to be presentation, diagnosis, and treatment delays, respectively. We compared lengths of intervals using Mann-Whitney U tests and logistic regression. RESULTS The most frequent cancer types were pancreatic (32%) and colorectal (28%). Most patients presented at stages III (38%) and IV (30%). The median presentation interval was 84 days (IQR, 56-191), and 49% presented after 3 months or longer. The median diagnosis and treatment intervals were 0 (IQR, 0-8) and 7 (IQR, 0-23) days, respectively. There was no relationship between age, sex, education, or distance to tertiary hospital and presentation delay, but patients with stage III to IV versus I to II had higher odds of presentation delay (odds ratio [OR], 1.68 [95% CI, 1.13 to 2.50]). Among patients with pancreatic cancer, older patients were less likely to have a diagnosis delay (OR, 0.50 [95% CI, 0.25 to 0.98]). CONCLUSION About half of patients with GI cancer in Ile-Ife, Nigeria, did not present to tertiary hospitals until more than 90 days after noticing symptoms. Efforts are warranted to improve public knowledge of GI cancer symptoms and to strengthen health systems for prompt diagnosis and referral to specialty care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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".