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Record W3007410695 · doi:10.1136/jclinpath-2019-206370

Putting the patient at the centre of pathology: an innovative approach to patient education—MyPathologyReport.ca

2020· review· en· W3007410695 on OpenAlexaff
Anthea Lafrenière, Bibianna Purgina, Jason K. Wasserman

Bibliographic record

VenueJournal of Clinical Pathology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedical diagnosisMedicineVariety (cybernetics)Patient carePatient educationMEDLINEMedical educationPathologyComputer scienceNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.090
GPT teacher head0.425
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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