Gastric Cancer at a Nigerian Tertiary Referral Center: Experiences With Establishing an Institutional Cancer Registry
Bibliographic record
Abstract
BACKGROUND: In Nigeria, gastric cancer is the 10th most common and 9th most deadly malignancy. The limited availability of robust data makes further characterizing it challenging. The objective of this study was to assess the presentation, and management of gastric cancer in Nigeria using an institutional cancer registry. METHODS: We reviewed a prospective database of patients diagnosed with any gastric cancer at a single tertiary referral center over 15 years (2007-2022). Patients with suspected gastric cancer were surveyed for sociodemographics and then added to the institutional gastric cancer registry. Thereafter, periodic chart review and phone call was used to obtain investigation results, and survival data, respectively. Only patients with complete histopathology were included in analysis. RESULTS: 138 patients met inclusion criteria (mean age 55.3 years, 68.8% male). Patients typically presented with weight loss (119, 86.2%) and anorexia (92, 66.7%). Blood work (132, 95.7%) and ultrasound (80, 57.9%) were the most common investigations. Most fully staged patients presented with metastatic disease (39, 90.2%). Patients underwent at least one treatment modality (109, 79.0%), and most 54 (49.5%) underwent both chemotherapy and surgery. Patients undergoing surgery usually had resection of their tumor (58, 67.4%). The median time of follow-up was 45.6 months, and 51.4% (71) of patients were dead at that time point. CONCLUSION: Our gastric cancer database identified that most patients present with advanced disease and are undergoing at least one treatment modality. The next steps include initiatives to strengthen the quality of registry data, identify high-risk patients, and provide timely 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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".