Gastrointestinal cancer resistance to treatment: the role of microbiota
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
The most common illnesses that adversely influence human health globally are gastrointestinal malignancies. The prevalence of gastrointestinal cancers (GICs) is relatively high, and the majority of patients receive ineffective care since they are discovered at an advanced stage of the disease. A major component of the human body is thought to be the microbiota of the gastrointestinal tract and the genes that make up the microbiome. The gut microbiota includes more than 3000 diverse species and billions of microbes. Each of them has benefits and drawbacks and been demonstrated to alter anticancer medication efficacy. Treatment of GIC with the help of the gut bacteria is effective while changes in the gut microbiome which is linked to resistance immunotherapy or chemotherapy. Despite significant studies and findings in this field, more research on the interactions between microbiota and response to treatment in GIC are needed to help researchers provide more effective therapeutic strategies with fewer treatment complication. In this review, we examine the effect of the human microbiota on anti-cancer management, including chemotherapy, immunotherapy, and radiotherapy.
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) | 0.001 | 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