Training and accreditation standards for pathologists undertaking clinical trial work
Bibliographic record
Abstract
Clinical trials rely on multidisciplinary teams for successful delivery. Pathologists should be involved in clinical trial design from the outset to ensure that protocols are optimised to deliver maximum data collection and translational research opportunities. Clinical trials must be performed according to the principles of Good Clinical Practice (GCP) and the trial sponsor has an obligation to ensure that all of the personnel involved in the trial have undergone training relevant to their role. Pathologists who are involved in the delivery of clinical trials are often required to undergo formal GCP training and may additionally undergo Good Clinical Laboratory Practice training if they are involved in the laboratory analysis of trials samples. Further training can be provided via trial-specific investigator meetings, which may be either multidisciplinary or discipline-specific events. Pathologists should also ensure that they undertake External Quality Assurance schemes relevant to the area of diagnostic practice required in the trial. The level of engagement of pathologists in academia and clinical trials research has declined in the United Kingdom over recent years. This paper recommends the optimal training and accreditation for pathologists undertaking clinical trials activities with the aim of facilitating increased engagement. Clinical trials training should ideally be provided to all pathologists through centrally organised educational events, with additional training provided to pathologists in training through local postgraduate teaching. Pathologists in training should also be strongly encouraged to undertake GCP training. It is hoped that these recommendations will increase the number of pathologists who take part in clinical trials research in order to ensure a high level and standard of data collection and to maximise the translational research opportunities.
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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 | 0.388 | 0.459 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.023 | 0.036 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".