A “chamber of errors” adaptation to assess pharmaceutical assistants’ knowledge in chemotherapy preparation
Bibliographic record
Abstract
French preparation guidelines state that pharmacy staff who manipulate cytotoxic drugs have to follow specific training. In order to assess the pharmaceutical assistants' skills and knowledge, we developed a "Cytotoxic Preparation Centralized Unit (CPCU) of errors," derived from the Canadian concept of "Chamber of horrors." A table listing 20 mistakes to track down was created and each pharmaceutical assistant spent 20 min in the "CPCU of errors" with the pharmacist, who wrote down the spotted mistakes in real time. Among the 21 trained pharmaceutical assistants, 15 were evaluated. On average, 11.9 mistakes on 20 were detected. The lowest score was 7 spotted errors on 20 and the highest was 16 on 20. Those results should be qualified depending on pharmaceutical assistants' years of experience in the preparation of chemotherapy. Those results may be explained by the way the role-playing was conducted. The simulation was not conducted during an actual preparation using the usual equipment. One of the major obstacles was the difficulty to clear some time for this project because its realization required a full-time pharmacist and the referring pharmaceutical assistant in addition to the evaluated pharmaceutical assistants. Overall, the staff feedback was positive and the role-playing led to a reminder of theoretical knowledge and the good use of some devices. It would be interesting to develop this type of project through a regional oncology network to create a medium that can be used by other hospitals.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".