Digital pathology access and usage in the UK: results from a national survey on behalf of the National Cancer Research Institute’s CM-Path initiative
Bibliographic record
Abstract
AIM: To canvass the UK pathology community to ascertain current levels of digital pathology usage in clinical and academic histopathology departments, and prevalent attitudes to digital pathology. METHODS: A 15-item survey was circulated to National Health Service and academic pathology departments across the UK using the SurveyMonkey online survey tool. Responses were sought at a departmental or institutional level. Where possible, departmental heads were approached and asked to complete the survey, or forward it to the most relevant individual in their department. Data were collected over a 6-month period from February to July 2017. RESULTS: 41 institutes from across the UK responded to the survey. 60% (23/39) of institutions had access to a digital pathology scanner, and 60% (24/40) had access to a digital pathology workstation. The most popular applications of digital pathology in current use were undergraduate and postgraduate teaching, research and quality assurance. Investigating the deployment of digital pathology in their department was identified as a high or highest priority by 58.5% of institutions, with improvements in efficiency, turnaround times, reporting times and collaboration in their institution anticipated by the respondents. Access to funding for initial hardware, software and staff outlay, pathologist training and guidance from the Royal College of Pathologists were identified as factors that could enable respondent institutions to increase their digital pathology usage. CONCLUSION: Interest in digital pathology adoption in the UK is high, with usage likely to increase in the coming years. In light of this, pathologists are seeking more guidance on safe usage.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".