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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it