Understanding and supporting histopathology slide sorting
Bibliographic record
Abstract
Histopathology laboratories devote considerable time and effort to sorting tissue sample slides. We observed slide sorting in a typical urban hospital to understand the existing workflow and explore how it might be supported by an interactive computer support system. We observed 8.5 hours of slide sorting activity through a video camera mounted above a laboratory workbench. Through detailed video analysis, we characterised the process, examined which activities took the most time, and explored design considerations. We found that a very large proportion (23.5%) of the slide sorting time involved managing paper documents. We suggest that an interactive computer support system could automatically detect which slides are sorted into which folders and digitally list additional slides to include with these sets; this would support the workflow of technicians, while eliminating paper management and manual barcode reading operations, leading to time savings of approximately 30%. Additional recommendations for the design of such a support system include focusing on case management (e.g. how many slides belong to each case, whether a complete case will fit within the current folder, and which slides associated with a case are still missing), supporting recovery from disruptions, and enabling a flexible rather than a highly scripted workflow.
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.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".