International Cancer Microbiome Consortium consensus statement on the role of the human microbiome in carcinogenesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: In this consensus statement, an international panel of experts deliver their opinions on key questions regarding the contribution of the human microbiome to carcinogenesis. DESIGN: International experts in oncology and/or microbiome research were approached by personal communication to form a panel. A structured, iterative, methodology based around a 1-day roundtable discussion was employed to derive expert consensus on key questions in microbiome-oncology research. RESULTS: Some 18 experts convened for the roundtable discussion and five key questions were identified regarding: (1) the relevance of dysbiosis/an altered gut microbiome to carcinogenesis; (2) potential mechanisms of microbiota-induced carcinogenesis; (3) conceptual frameworks describing how the human microbiome may drive carcinogenesis; (4) causation versus association; and (5) future directions for research in the field.The panel considered that, despite mechanistic and supporting evidence from animal and human studies, there is currently no direct evidence that the human commensal microbiome is a key determinant in the aetiopathogenesis of cancer. The panel cited the lack of large longitudinal, cohort studies as a principal deciding factor and agreed that this should be a future research priority. However, while acknowledging gaps in the evidence, expert opinion was that the microbiome, alongside environmental factors and an epigenetically/genetically vulnerable host, represents one apex of a tripartite, multidirectional interactome that drives carcinogenesis. CONCLUSION: Data from longitudinal cohort studies are needed to confirm the role of the human microbiome as a key driver in the aetiopathogenesis of cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it