MétaCan
Menu
Back to cohort
Record W4414300660 · doi:10.12927/hcpol.2025.27663

Canadian Family Physician Preferences on Updating the Classification System for Health Conditions and Related Issues

2025· article· en· W4414300660 on OpenAlexaffvenueabout
Stephanie Garies, Kerry McBrien, Noah Crampton, Kees van Boven, Keith Denny, Terrence J. McDonald, Hude Quan, Michelle Smekal, William A. Ghali, Tyler Williamson

Bibliographic record

VenueHealthcare policy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsCanadian Institute for Health InformationUniversity of ManitobaUniversity of TorontoUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsCoding (social sciences)VignetteMedical diagnosisWorkloadMedical classificationHealth careFocus group

Abstract

fetched live from OpenAlex

Physician billing claims are used to inform health system planning and for other secondary purposes. In most provinces/territories, diagnoses are coded using a system adopted in 1979, the International Classification of Diseases version 9 (ICD-9). This study aimed to understand the perspectives of family physicians on updating ICD-9. Canadian family physicians completed an online patient vignette coding exercise and electronic survey to capture preferences on two newer coding systems (ICD-11; International Classification for Primary Care version 3 [ICPC-3]), compared with the current ICD-9 system. The focus of this paper is the survey data, which were analyzed descriptively. One hundred and sixty-one family physicians from six provinces participated. Over half of them (58%) stated that ICD-9 should be replaced, and 86% of them felt confident learning a new coding system. After the coding exercise, most participants reported that they were very or somewhat satisfied with both newer systems (77% for ICD-11; 73% for ICPC-3). Family physicians in our study support replacing the outdated ICD-9 system to better reflect their workload and patient complexity. This paper provides recommendations for provinces/territories considering modernizing physician billing requirements.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.232
GPT teacher head0.513
Teacher spread0.281 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueHealthcare policySame topicMedical Coding and Health InformationFrench-language works237,207