Current status of discrete data capture in synoptic surgical pathology and cancer reporting
Bibliographic record
Abstract
Christopher L Williams,1 Roger Bjugn,2 Lewis A Hassell1 1Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; 2Department of Pathology, Oslo University Hospital, Oslo, Norway Abstract: The current status of synoptic pathology reporting is presented with its historical context. The awareness of additional audiences and users has made the presentation and capture of pathology data, particularly cancer data of broad importance. Current models of adoption in the US, Canada, Norway, and the Netherlands are noted. Significant terms, benefits, and stakeholders key to implementation and advancement of capabilities particularly with regard to capture of discrete data elements are presented. Important barriers to be overcome include fiscal constraints, technologic barriers such as interconnectivity and legacy systems, as well as social and organizational obstacles. Keywords: quality assurance, integrated disease reporting, clarity, completeness, pathology report, cancer registry, biorepository
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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 | 0.336 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".