Informatics for practicing anatomical pathologists: marking a new era in pathology practice
Bibliographic record
Abstract
Informatics can be defined as using highly advanced technologies to improve patient diagnosis or management. Pathology informatics had evolved as a response to the overwhelming amount of information that was available, in an attempt to better use and maintain them. The most commonly used tools of informatics can be classified into digital imaging, telepathology, as well as Internet and electronic data mining. Digital imaging is the storage of anatomical pathology information, either gross pictures or microscopic slides, in an electronic format. These images can be used for education, archival, diagnosis, and consultation. Virtual microscopy is the more advanced form of digital imaging with enhanced efficiency and accessibility. Telepathology is now increasingly becoming a useful tool in anatomical pathology practice. Different types of telepathology communications are available for both diagnostic and consultation services. The spectrum of applications of informatics in the field of anatomical pathology is broad and encompasses medical education, clinical services, and pathology research. Informatics is now settling on solid ground as an important tool for pathology teaching, with digital teaching becoming the standard tool in many institutions. After a slow start, we now witness the transition of informatics from the research bench to bedside. As we are moving into a new era of extensive pathology informatics utilization, several challenges have to be addressed, including the cost of the new technology, legal issues, and resistance of pathologists. It is clear from the current evidence that pathology informatics will continue to grow and have a major role in the future of our specialty. However, it is also clear that it is not going to fully replace the human factor or the regular microscope.
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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 | 0.042 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.024 | 0.044 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".