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Record W1999673196 · doi:10.1155/2012/734202

Care for Patients with Type 2 Diabetes in a Random Sample of Community Family Practices in Ontario, Canada

2012· article· en· W1999673196 on OpenAlexaffabout
Gina Agarwal, Janusz Kaczorowski, Steve Hanna

Bibliographic record

VenueInternational Journal of Family Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversité de MontréalMcMaster University
Fundersnot available
KeywordsMedicineDiabetes mellitusGlycemicType 2 diabetesFamily medicineAuditBlood pressureMedical recordPediatricsInternal medicine

Abstract

fetched live from OpenAlex

Objective. Diabetes care is an important part of family practice. Previous work indicates that diabetes management is variable. This study aimed to examine diabetes care according to best practices in one part of Ontario. Design and Participants. A retrospective chart audit of 96 charts from 18 physicians was conducted to examine charts regarding diabetes care during a one-year period. Setting. Grimsby, Ontario. Main Outcome Measures. Glycemic screening, control and management strategies, documentation and counselling for lifestyle habits, prevalence of comorbidities, screening for hypertension, hyperlipidemia, and use of appropriate recommended preventive medications in the charts were examined. Results. Mean A1c was within target (less than or equal to 7.00) in 76% of patients (ICC = -0.02), at least 4 readings per annum were taken in 75% of patients (ICC = 0.006). Nearly 2/3 of patients had been counselled about diet, more than 1/2 on exercise, and nearly all (90%) were on medication. Nearly all patients had a documented blood pressure reading and lipid profile. Over half (60%) had a record of their weight and/or BMI. Conclusion. Although room for improvement exists, diabetes targets were mainly reached according to recognized best practices, in keeping with international data on attainment of diabetes targets.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.329
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2012
Admission routes2
Has abstractyes

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