Pathology Informatics Trends in Anatomical Pathology: Analysis of 11 Years of United States and Canadian Academy of Pathology Abstracts
Bibliographic record
Abstract
Pathology informatics is a relatively new field. Applications such as digital imaging, web-based tools, and database management in anatomic pathology have increased with advances in technology. The aim of this study was to review a decade of United States & Canadian Academy of Pathology (USCAP) abstracts to investigate these trends. Microsoft Excel 2010 was used for data entry and analysis. All USCAP abstracts from 2003 to 2013 published in Modern Pathology supplements were reviewed. Informatics-related abstracts were identified and classified into telepathology, whole slide imaging (WSI), image analysis software, database analysis software, web-based pathology, advance imaging, and miscellaneous submissions. Of the 19,588 total abstracts published by the USCAP during this time period, 638 (3.25%) primarily involved informatics. The number of informatics abstracts almost tripled during the last decade. They increased each year from 2.17% (33/1516) in 2003 to 4.37% (92/2102) in 2013. Most abstracts emanated from the United States (85.4%), with other contributions originating from Canada (6.43%), Europe (5.04%), Asia (2.5%) and South America (0.63%). The most noticeable informatics topics covered during this time frame were related to imaging informatics (450; 70.53%) including telepathology, whole slide imaging, image analysis, and advanced imaging. Other topics were database analysis software (129; 20.22%), web-based pathology (23; 3.61%), and miscellaneous topics (36; 5.64%). Digital imaging abstracts increased markedly over the last decade. Advanced imaging studies appeared only after 2010. These data show, as predicted, an increase in the proportion of informatics related abstracts being presented at annual USCAP meetings.
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 | 0.019 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.119 | 0.095 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".