Cancer Registration in an LMIC: Insights from a Comprehensive Cancer Center in Luxor
Bibliographic record
Abstract
Abstract Introduction Cancer registries are vital for informing cancer control, especially in low- and middle-income countries (LMICs) where data is limited. This study describes the Shefa Al Orman Hospital (SOH) Cancer Registry in Luxor, Egypt, which follows ICD-O3, SEER staging, and Toronto pediatric staging. Methods A prospective cohort registry was established at SOH from May 2016 to May 2024. Data were collected via house made electronic health records and included demographics, tumor site, morphology, stage, diagnosis method, survival, and geolocation. Descriptive statistics and crude rate calculations were used to analyze distributions and regional patterns. Results A total of 44,134 patients were registered: 42,807 adults and 1,327 children. Patients came from Luxor (44.7%), Qena (34.2%), and other governorates (21%). There were 22,479 (50.9%) cancer cases, 9,203 (20.8%) non-cancer (reflecting weak referral systems and limited primary care), 1,106 (2.5%) deaths before diagnosis (delayed presentation/referral), 6,737 (15.2%) lost to follow-up (suggesting fear or low awareness), and 2,491 (5.6%) referred. In females, top cancers were breast (42.8%), digestive (15.9%), and female genital (9.4%). In males: digestive (30.5%), respiratory (13.7%), and urinary (13.4%). Annual crude cancer rates per 100,000 (8 years): Luxor: Qesm Luxor(246.5), Armant(191.6), Tiba(184.4), Qurna(178.2), Luxor Markaz(162.7), Esna(136.0). Qena: Southern Qena: Qus (110.3), Naqada (97.0), Qeft (76.4). Northern Qena: Qena Markaz (43.4), Farshout (39.7), Deshna (38.1), Nag Hammadi (48.2) The lower rates in northern Qena likely reflect that southern Qena residents primarily attend SOH, while those in the north often seek care in Sohag Conclusions The SOH registry shows that high-quality cancer registration is feasible in LMICs. It highlights the need for better early detection, awareness, referral efficiency, and care coordination, offering a model for cancer control in resource-limited settings. Key messages • Cancer registries in LMICs can reveal critical gaps in referral, diagnosis, and follow-up that impact timely cancer care. • SOH registry data supports early detection and improved care coordination in underserved regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".