Putting the patient at the centre of pathology: an innovative approach to patient education—MyPathologyReport.ca
Bibliographic record
Abstract
In many centres, patients now have access to their electronic medical record (EMR) and laboratory results, including pathology reports, are amongst the most frequently accessed pieces of information. The pathology report is an important but highly technical medical document that can be difficult for patient and clinicians alike to interpret. To improve communication and patient safety, pathologists are being called upon to play a more direct role in patient care. Novel approaches have been undertaken by pathologists to address this need, including the addition of patient-friendly summaries at the beginning of pathology reports and the development of patient education tools. MyPathologyReport.ca is a novel website exclusively providing pathology education to patients. It has been designed to help patients understand the language of pathology and to effectively navigate their pathology report. At present, the website includes over 150 diagnostic articles and over 125 pathology dictionary definitions. The diagnostic articles span all body sites and include a variety of malignant, benign and non-neoplastic conditions. Since its creation, this website has been visited over 14 000 times, with cancer-related diagnoses and definitions representing the most commonly accessed articles. This website has been embedded in patient accessible EMRs and shared through partnerships with patients, caregivers and their respective advocacy groups. Our next steps involve longitudinal assessment of MyPathologyReport.ca from non-medical community members, evaluation of patient satisfaction and understanding and further collaboration with hospitals and care-providers to increase patient access to this resource.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.031 |
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".