The Association of Human Cytomegalovirus Infection and Colorectal Cancer: A Clinical Analysis
Bibliographic record
Abstract
Background: Human cytomegalovirus (HCMV) commonly infects humans and establishes lifelong infection. It causes disease and increased mortality rates in patients with immunosuppression. HCMV gene products are found to be present in multiple human malignancies and target cellular functions involved in tumor development; additionally, a tumor-cytoreductive role of CMV has also been observed. The purpose of this study was to evaluate the correlation between CMV infection and the incidence of colorectal cancer (CRC). Methods: The data were provided by a national database that is compliant with Health Insurance Portability and Accountability Act (HIPAA). Using International Classification of Disease (ICD)-10 and ICD-9 diagnostic codes, the data were filtered to evaluate patients infected with HCMV versus patients never infected with HCMV. Patient data from 2010 to 2019 were assessed. Access to the database was granted by Holy Cross Health, Fort Lauderdale for the purpose of academic research. Standard statistical methods were used. Results: Between January 2010 and December 2019, the query was analyzed and resulted in 14,235 patients after matching in the infected and control groups. The groups were matched by age range, sex, Charlson Comorbidity Index (CCI) score, and treatment. The incidence of CRC was 1.159% (165 patients) in the HCMV group and 2.845% (405 patients) in the control group. The difference after matching was statistically significant by a P-value < 2.2 × 10 -16 with an odds ratio of 0.37 (95% confidence interval (CI) 0.32 - 0.42). Conclusions: The study shows a statistically significant correlation between CMV infection and a reduced incidence of CRC. Further evaluation is recommended to assess the potential of CMV in reducing CRC incidence. World J Oncol. 2023;14(2):119-124 doi: https://doi.org/10.14740/wjon1565
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".