Fusobacterium nucleatum tumor DNA levels are associated with survival in colorectal cancer patients
Bibliographic record
Abstract
There is increasing evidence indicating a role for Fusobacterium nucleatum (F. nucleatum) in colorectal cancer (CRC) development and prognosis. This study evaluated F. nucleatum as a prognostic biomarker, by assessing its association with post-diagnosis survival from CRC. From September 2008 to April 2012 CRC patients (n = 190) were recruited from three hospitals within the Czech Republic. F. nucleatum DNA copies were measured in adjacent non-malignant and colorectal tumor tissues using quantitative real-time PCR. Cox Proportional Hazards (HR) models were applied to evaluate the association between F. nucleatum DNA and overall survival, adjusting for key confounders. Risk prediction modeling was conducted to evaluate the ability to predict survival based on F. nucleatum status. High, compared with low, levels of F. nucleatum in colorectal tumor tissues were associated with poorer overall survival (adjusted HR 1.68, 95% CI 1.02-2.77), which was slightly attenuated after additional adjustment for microsatellite instability status. However, inclusion of F. nucleatum in risk prediction models did not improve the ability to identify patients who died beyond known prognostic factors such as disease pathology staging. Although the increased presence of F. nucleatum was associated with poorer prognosis in CRC patients, this may have limited clinical relevance as a prognostic biomarker.
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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) | 0.001 | 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".