Geo-epidemiological reporting and spatial clustering of the 10 most prevalent cancers in Iran
Bibliographic record
Abstract
Cancer is a problem of both global and local concern. We determined the geo-epidemiological and spatial distribution of the 10 most common cancers in Iran. We used the data of the Iranian Cancer Registry for the year 2014 analysing the prevalence of 112,131 registered cancer cases with the aim of detecting potential geographical underlying causes. The geographic distribution of cancers is reported as standardized incidence rates at the provincial level considering risk with respect to sex and age. A geographical information systems (GIS) approach based on Anselin Local Moran's index method was used to map clusters and spatial autocorrelation patterns. The mean age of the patients was 55.6 (±17.8) and 61.7 (±18.2) for females and males, respectively, in the database which showed 46.1% (n=51,665) of all cases to be female. Analysis of the spatial distribution of cancers showed significant differences among the different provinces. Stomach and breast cancers were the most prevalent cancers in men and females, respectively. The highest incidence rates of stomach cancer were found in Ardabil and Zanjan provinces, with 48.38 and 48.08 per 100,000 population, respectively, while Tehran and Yazd provinces had the highest incidences of breast cancer, 51.0 and 47.5 per 100,000 population, respectively. Strong clustering patterns for stomach and breast cancers were identified in the north-western provinces and in Semnan Province, respectively. These patterns indicate a diversity of geo-epidemiological contributing factors to cancer incidence in Iran.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".