Bibliographic record
Abstract
OBJECTIVES: To educate pharmacists about the application of laboratory values in oncology. METHODS: Research on drugs used in cancer therapy was conducted using multiple sources, including primary, secondary and tertiary references. Online searches were conducted on Medline (1966-2004), EMBASE (1996-2004) and Ovid databases, using a drug's generic name and key words, such as 'adverse effects', 'hematotoxicity', 'renal toxicity', 'hepatotoxicity', 'cardiotoxicity', 'organ dysfunction', and terms describing chemotherapy-related toxicity, such as 'tumour lysis syndrome'. RESULTS: Laboratory monitoring in oncology was separated into the hematologic, hepatic, renal, cardiovascular and pulmonary systems. Laboratory tests applicable to each system are discussed. In addition, tests pertaining to specific drugs used in cancer therapy are explained. This information was compiled into a comprehensive continuing pharmacy education module. CONCLUSION: Laboratory monitoring assists the pharmacist in the monitoring of chemotherapy. A general understanding of common tests used in cancer therapy and knowledge specific to drugs used can help the pharmacist tailor drug therapy monitoring.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".