Prediction of Colorectal Cancer Incidence Rate in the Counties of Fars Province, Iran: An Application of Small Area Estimation
Bibliographic record
Abstract
Background: Colorectal cancer (CRC) is one of the main causes of mortality and morbidity worldwide. Socio-economic status is one of the most important related factors with CRC. Objectives: In this study, we used the human development index (HDI) as one of the common measures of socio-economic status to predict the incidence rate of CRC in the counties of Fars Province in Iran. Methods: In this ecological study, we used the medical records of 108 patients with CRC from Fars province, who referred to Shahid Faghihi Hospital in Shiraz from January 2011 to March 2013. Since sample sizes were not efficient in all the counties, we used the log-normal model within small area estimation framework to have a reliable prediction for the incidence rate in each county. As using related auxiliary variables is necessary in small area models, we considered the HDI of counties as an auxiliary variable. Results: The findings showed that there was a significant direct relationship between HDI and CRC incidence rate. Furthermore, the highest predicted rates were observed in the northern and eastern parts of the province. Conclusions: In order to compensate the deficiency of sample size in some of the counties, we used a small area model to predict the CRC incidence rate. The highest incidence rates mostly occurred in the counties with the highest HDI. It is observed that the counties with higher incidence rates are closer to more industrial provinces and the counties with lower incidence rates are closer to less industrial provinces. So, it seems that development disparities strongly affected the incidence rates.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".