Pharmacy Workload in Clinical Trial Management: A Preliminary Complexity Assessment Tool for Sponsored Oncology and Haematology Trials
Bibliographic record
Abstract
Investigational drug services need to be organised in a structured approach, especially for sites with a large number of ongoing clinical trials. The aim of this study was to develop a tool to assess the complexity of pharmacy involvement in a sponsored oncology clinical trial. Categorisation into ordinal complexity categories was used to assess the complexity of the clinical trials for consistent pharmacy grant applications. The 15 items of the tool were divided into three sections, and individual item scores were agreed upon among four pharmacists with experience in the conduct of clinical trials at two different centres. A final version of the tool, named Pharm-CAT, was approved. The pharmacists were instructed to use Pharm-CAT to assign a score to each new sponsored trial. To determine the cut-offs for the complexity categories, the scores were sorted in ascending order and the cut-offs corresponding to the first and third tertiles of the score distribution were selected. To verify the reproducibility of the results, Pharm-CAT was applied by two pharmacists independently for each trial. Pharm-CAT proved to be user-friendly. Sixty clinical trials were evaluated and a total of 120 scores were recorded. Low-complexity scores ranged from 0 to 19, medium-complexity scores ranged from 20 to 25, and high-complexity scores were 26 or higher. The average score recorded was 22.88 points. Prospective multicentre validation of Pharm-CAT is needed to confirm its applicability.
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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 | 0.030 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".