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Record W3047917416 · doi:10.12927/hcq.2020.26271

Supporting Asthma and COPD Best Practices in Ontario through Primary Care Electronic Medical Records

2020· article· en· W3047917416 on OpenAlexaffvenueabout
Ann K. Taite, Janice P. Minard, Alison Morra, M. Diane Lougheed

Bibliographic record

VenueHealthcare Quarterly · 2020
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsASTERKingston General Hospital
Fundersnot available
KeywordsCOPDAsthmaMedicineMedical recordPrimary careElectronic medical recordPulmonary diseaseBest practiceWork (physics)Family medicineAsthma managementMedical emergencyInternal medicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Asthma and chronic obstructive pulmonary disease (COPD) are two of the most common chronic respiratory diseases. Electronic medical records (EMRs) are increasingly being used as a means to support chronic disease management and enable best practices. This article summarizes the state of asthma and COPD care and electronic solutions in Ontario, highlighting the work done to date for asthma and COPD data standards. Lessons learned and future considerations for the use of EMRs and related electronic solutions to support chronic disease management are discussed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.445
Teacher spread0.357 · 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
GenreEmpirical

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

Citations8
Published2020
Admission routes3
Has abstractyes

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