Bibliographic record
Abstract
PURPOSE: To provide a current review of the literature related to chemotherapy induced diarrhea (CID), including clinical assessment, recommended management guidelines and investigational pharmacological approaches for the prevention and treatment of CID. DATA SOURCES: A search of MEDLINE, PubMed, EMBASE, Cochrane Library, International Pharmaceutical Abstracts, and Web of Science (1996-2006) databases was conducted using terms such as: chemotherapy, diarrhea, diarrhoea, and irinotecan. Appropriate references from selected articles were also used. The search engine, Google, provided further access to information. DATA EXTRACTION: The retrieved literature was reviewed to include all articles pertaining to the pathophysiology, assessment and management of CID. DATA SYNTHESIS: Diarrhea is a debilitating and potentially life-threatening side effect associated with many chemotherapeutic agents. Despite the high incidence and severity of CID, it is often under recognized and poorly managed. A multidisciplinary panel recently updated recommended practice guidelines for the assessment and management of CID. Prompt and aggressive intervention is important in order to minimize the negative consequences of CID, such as dehydration, which may cause interruptions in optimal clinical outcomes or may lead to life-threatening sequelae. Further investigation into the pathophysiology of CID may allow for more directed approaches in the prophylaxis and treatment of CID.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".