Creation of a Retrospective Searchable Neuropathologic Database from Print Archives: The UHN Experience
Bibliographic record
Abstract
UHN provides a large proportion of the neuro‐oncologic care for the 10 million people in Ontario. The paper, glass slide and paraffin block archival material for this center is available from the 1930's to present. A searchable electronic database was instituted prospectively in September 2001. To add the previous 75 years (more than 50,000 specimens) to the database, we employed high‐throughput automated scanning of the paper archives. The searchable PDF files generated from these scans were filtered through a multi‐tiered process driven by Java computer programs that selected relevant patient information and diagnostic information. A second series of programs searched the pathologist‐assigned diagnoses and was capable of converting more than ninety percent of these to the standardized WHO format. This was achieved with a simple master list of key site and diagnostic terms, and prioritization rules determined on a trial and error basis. Within three months, the categorization of over 5000 cases in an easily searchable format was completed and an additional 20,000 to 30,000 cases were accrued in PDF format pending completion.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".