Current usage and future trends in gross digital photography in Canada
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to assess the current usage, utilization and future direction of digital photography of gross surgical specimens in pathology laboratories across Canada. METHODS: An online survey consisting of 23 multiple choice and free-text questions regarding gross digital photography was sent out to via email to laboratory staff across Canada involved in gross dissection of surgical specimens. RESULTS: Sixty surveys were returned with representation from most of the provinces. Results showed that gross digital photography is utilized at most institutions (90.0%) and the primary users of the technology are Pathologists (88.0%), Pathologists' Assistants (54.0%) and Pathology residents (50.0%). Most respondents felt that there is a definite need for routine digital imaging of gross surgical specimens in their practice (80.0%). The top two applications for gross digital photography are for documentation of interesting/ complex cases (98.0%) and for teaching purposes (84.0%). The main limitations identified by the survey group are storage space (42.5%) and security issues (40.0%). Respondents indicated that future applications of gross digital photography mostly include teaching (96.6%), presentation at tumour boards/ clinical rounds (89.8%), medico-legal documentation (72.9%) and usage for consultation purposes (69.5%). CONCLUSIONS: The results of this survey indicate that pathology staff across Canada currently utilizes gross digital images for regular documentation and educational reasons. They also show that the technology will be needed for future applications in teaching, consultation and medico-legal purposes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".