The Evolution of Clostridium difficile Infection in Cancer Patients: Epidemiology, Pathophysiology, and Guidelines for Prevention and Management
Bibliographic record
Abstract
Clostridium difficile infection (CDI) has emerged as a significant challenge to the healthcare system. The availability of anti-cancer chemotherapeutic regimens has contemporaneously resulted in a larger population of patients who are susceptible to CDI. The outbreak of a novel, hypervirulent, resistant strain, NAP-1/027 as well as resistance to antibiotic therapy have further contributed to an increase in prevalence as well as in disease severity. Recent data show high fatality rates in cancer patients with CDI. In this review, we have discussed the incidence, epidemiology, pathophysiology, clinical signs and symptoms and therapeutic guidelines for patients who are on chemotherapy and present with CDI and highlighted clinical reports documenting severe CDI associated with chemotherapeutic agents such as methotrexate, 5FU, cisplatin, carboplatin, paclitaxel, vinorelbine and cyclophosphamide. The review article also has the discussion of patents pertaining to infections caused by Clostridium difficile in cancer patients. We underscore the urgent need for early recognition and diagnosis of CDI in cancer patients and for the design and implementation of randomized clinical trials of new treatment modalities in the management of chemotherapy- associated CDI.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".