Rationalizing the use of auxiliary label for oral oncology drugs
Bibliographic record
Abstract
Objective The objective of this study is to develop a systematic approach to standardize the use of auxiliary labels for oral oncology drugs. Design The project was multi-phased: environmental scan of auxiliary labels used at six BC Cancer Agency centre pharmacies, develop guidelines to support auxiliary labels standardization, develop inclusion criteria for common warnings and standardize warnings based on guiding principles and evidence (Canadian Compendium of Pharmaceutical Specialties, BC Cancer Agency Cancer Drug Manual, British National Formulary, literature). Results Consistent auxiliary labels use was rare (7% of drugs). No explicit methodology for determining previous auxiliary labels use was identified. Guiding principles developed include auxiliary labels supplement counselling and drug-specific patient handouts; a maximum of four auxiliary labels (limited container size and alert fatigue); identify hazardous drugs with auxiliary labels; auxiliary labels not intended for universal warnings (e.g., keep out of reach of children); warnings prioritized by impact on storage, efficacy (e.g., administration instructions), toxicity (including interactions) and other clinical issues. Inclusion criteria were developed for warnings on pregnancy, crushing/chewing, taking with plenty of water, drowsiness/dizziness, alcohol, grapefruit juice, hazardous and sunlight exposure. First list of standardized auxiliary labels was completed in June 2014. Conclusion A systematic approach was developed to determine and prioritize auxiliary labels for oral oncology drugs. This has led to a standardized and more accurate labelling throughout the six BC Cancer Agency centres' pharmacies.
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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 | 0.210 | 0.290 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".