Prescribing practices of oncology pharmacists working in ambulatory cancer centers in Alberta
Bibliographic record
Abstract
OBJECTIVE: To describe and quantify independent prescribing of oncology pharmacists working in adult, ambulatory cancer centers in Alberta, Canada. METHODS: was conducted. Prescriptions from January 1, 2018 to June 30, 2018 were analyzed. Descriptive statistics were used to quantify prescription volume and class of medications prescribed. A cross-sectional analysis was then performed on a random sample to determine the type of prescription intervention and evaluate pharmacist documentation. RESULTS: Over 6 months, 3474 prescriptions were ordered by 33 clinically deployed pharmacists. The median number of medications prescribed was 7 per month (interquartile range: 1.50-27.00; Range: 0.17-79.5). When prescribing was standardized by pharmacist's time clinically deployed, the median was 21.67 (interquartile range: 5.00-79.67; range: 0.67-216.67) prescriptions per month per full-time equivalent. The most prescribed class of medication was antiemetic (24.1%). From a sample of 346 prescriptions, 172 (50%) were new medications initiated, 160 (46%) were the continuation of existing prescriptions and 14 (4%) were prescription dosage adjustments. Adherence to the specified documentation standards was 47%. CONCLUSIONS: Oncology pharmacists utilize their independent prescribing to initiate and continue supportive care medications for cancer patients. The prescribing volume varied greatly among pharmacists. Opportunities exist to further engage pharmacist prescribing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".