Practical issues in the use of systemic anti-cancer therapy drugs
Bibliographic record
Abstract
Introduction The role of systemic anti-cancer therapy (SACT) in the management of cancer is evolving rapidly with widening indications for treatment and, in many diagnoses, additional therapies and lines of treatment now available. In 2015, there are now over 140 drugs licensed to be used for cancer treatment and it is not practical within this chapter to give a comprehensive description of each drug or treatment regimen. More detailed information can be found in chemotherapy textbooks, at the manufacturers' websites, the electronic Medicines Compendium (eMC) or from oncology pharmacy websites (e.g. http://www.medicines.org.uk/emc and www.bccancer.bc.ca, accessed January 2015). However, we hope this chapter, which focuses mainly on classic cytotoxic chemotherapy drugs, will provide SACT prescribers, pharmacists and administrators with sufficient information to discuss treatment with patients, to prescribe and deliver drugs safely and to recognise common treatment-related side effects. Over the last decade there has been a major increase in activity and workloads within chemotherapy treatment units. The 2009 National Cancer Advisory Group report described an increase in overall activity of 60% in just a four year period (NCAG, 2009). This rise in activity is in part a result of increased numbers of patients but there has also been a major expansion in the indications for which there is effective treatment, the upper age range of patients treated and, in many malignancies, the number of lines of therapy available for use. Whilst the newer drugs are predominantly oral agents, the recent development of maintenance monoclonal antibody therapies for breast cancer and non-Hodgkin lymphoma and the more modern prolonged and complex regimens in gastrointestinal malignancies have added considerable pressure to the workload of pharmacy and chemotherapy treatment units. A summary of the rapid increase in both the number of new cancer treatment drugs and the change in identity of new SACT agents can be see in Table 1.1 that shows both the historical and modern trends in new cancer drugs. This demonstrates the change from the initial cancer treatment drugs of the 1970s/80s/90s that were predominantly classic cytotoxic chemotherapy agents to a new, varied range of agents including monoclonal antibodies, TKI and MTOR inhibitors and other new agents (Savage and Mahmoud, 2013).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".