<i>Hemophilus influenzae</i> Infection’s Association With Decreased Risk of Breast Cancer
Bibliographic record
Abstract
Background: Hemophilus influenzae ( H. influenzae ) is a common cause of widespread bacterial infections and has been associated with the stabilization of the microbiome. The microbiome, through modulating systemic inflammation with possible upregulation of the NLRP3 inflammasome, may potentiate the development of breast cancer (BC). The purpose of this study was to therefore evaluate the correlation between previous H. influenzae infection and the incidence of BC. Methods: A large national database was used to collect International Classification of Disease Ninth and Tenth Codes to evaluate the incidence of BC between January 2010 and December 2019 in patients with and without H. influenzae history. A retrospective cohort study was performed where these groups of individuals were matched by age range, Charlson Comorbidity Index (CCI), and antibiotic treatment exposure. Significance and relative risk were obtained using standard statistical procedures. Results: A total of 13,599 patients were matched by age range and CCI in both the experimental and control groups. BC incidence was 259 (1.905%) in the H. influenzae group compared to 686 (5.044%) in the control group (P < 2.2 × 10 -16 ; odds ratio (OR) = 0.604, 95% confidence interval (CI): 0.553 - 0.660). Matching by antibiotic treatment exposure resulted in two groups of 3,189 patients, in which BC incidence was 98 (3.073 %) in the H. influenzae group compared to 171 (5.362%) in the control group (P < 2.2 × 10 -16 ; OR = 0.584, 95% CI: 0.515 - 0.661). Conclusion: The study shows a statistically significant correlation between H. influenzae and a reduced incidence of BC. These results warrant further research regarding H. influenzae ’s role in upregulating the NLRP3 inflammasome and its potential role in BC prevention and treatment. World J Oncol. 2023;14(4):255-265 doi: https://doi.org/10.14740/wjon1617
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".