Enhancing patient understanding of pathology reports: Insights from a large database of patient inquiries.
Bibliographic record
Abstract
e13512 Background: The 21st Century Cures Act and the adoption of electronic health records have given patients immediate access to their diagnostic reports via patient portals. However, viewing pathology results before consulting with their physician can cause significant stress as patients struggle to interpret the technical language used in these reports. MyPathologyReport.ca is a website designed to help patients understand and navigate their pathology reports. The site features over 500 diagnostic articles and 400 pathology dictionary definitions. Since its inception, the site has been visited over 10 million times, with cancer-related content being the most popular. One unique feature is “Ask a Pathologist,” where patients can submit questions about their reports. Since 2022, over 6,000 emails have been submitted worldwide, providing valuable insights into common patient concerns. Moreover, a better understanding of these concerns can help us create tools aimed at improving patient literacy. Methods: A study protocol was created to analyze emails submitted to “Ask a Pathologist” on MyPathologyReport.ca. This protocol was reviewed by Patient Champions and Data Analytics committees. We analyzed 680 emails from 06/15/2024 to 01/14/2025, excluding 299 deemed to be irrelevant (spam, duplicates). Using ChatGPT, emails were summarized and sorted into categories: cancer or non-cancer-related, organ system, and treatment or diagnosis questions. Data was analyzed using pivot tables in Excel. Results: Of 381 relevant inquiries: 137 (36%) related to cancer diagnoses 214 (56%) related to non-cancer diagnoses 30 (8%) were not diagnosis-specific For cancer diagnoses, the most involved organ systems were: Breast (19%) GI (12%) Gynecologic (12%) Lymph node (11%) Skin (11%) Soft tissue (6 or 4%) Lung (5 or 4%) Thyroid (3%), Pancreas (2%) Bladder, Kidney, Liver, Brain, Adrenal, and Penis (1% each) 14 (10%) of cancer-related questions were about biomarkers. Most inquiries (78%) were diagnosis-related, with 23% about treatment and 18% about accessing diagnostic information. Conclusions: As pathology reports become more complex and accessible, there is a growing need for high-quality educational resources. MyPathologyReport.ca helps patients understand their reports, and the "Ask a Pathologist" section provides valuable data on common patient questions, aiding in the creation of high-yield content.
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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 | 0.017 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".