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Record W2807680423 · doi:10.2196/resprot.9588

The General Practitioner Prompt Study to Reduce Cardiovascular and Renal Complications in Patients With Type 2 Diabetes and Renal Complications: Protocol and Baseline Characteristics for a Cluster Randomized Controlled Trial

2018· article· en· W2807680423 on OpenAlexvenueno aff
Andrew Willis, Winston Crasto, Laura J. Gray, Helen Dallosso, Ghazala Waheed, Geri Gray, Melanie J. Davies, Kamlesh Khunti

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsMedicineType 2 diabetesRandomized controlled trialProtocol (science)Cluster randomised controlled trialCluster (spacecraft)Diabetes mellitusBaseline (sea)RandomizationPhysical therapyInternal medicineAlternative medicineComputer science

Abstract

fetched live from OpenAlex

Background: Adherence to evidence-based cardiovascular risk factor targets in patients with type 2 diabetes and microalbuminuria has shown long-term reduction in mortality and morbidity. Strategies to achieve such adherence have been delivered at individual patient level and are not cost-effective. Health care professional-level intervention has the potential to promote better adherence at lower cost. Objective: The aim of this study was to assess the effectiveness of a multifactorial technology-driven intervention comprising health care professional training, a software prompt installed on practice systems, clinician email support, and enhanced performance and feedback reporting. Methods: A cluster randomized trial will be performed where the primary outcome is the proportion of eligible patients meeting tight cardiovascular risk factor targets, including systolic and diastolic blood pressure (BP; BP<130/80 mm Hg) and total cholesterol (TC; TC<3.5 mmol/L) at 24 months. Secondary outcomes include proportion of patients with glycated hemoglobin (HbA1c) <58 mmol/mol (7.5%), change in medication prescribing, changes in microalbuminuria and renal function (estimated glomerular filtration rate, eGFR), incidence of major adverse CV events and mortality, and coding accuracy. Cost-effectiveness of the intervention will also be assessed. Results: Among 2721 eligible patients, mean age was 62.9 (SD 10.0) years, and duration of diabetes was 10.46 (SD 7.22) years. Mean HbA1c was 59.3 (SD 17.4) mmol/mol; mean systolic and diastolic BP (mm Hg) were 134.3 (SD 14.6) and 76.1 (SD 9.5) mm Hg, respectively; and mean TC was 4.1 (SD 0.98) mmol/L. Overall, 131 out of 2721 (4.81%) patients achieved all 3 “tight” cardiovascular risk factor targets. Cardiovascular risk factor burden increased two-fold in those with eGFR<60 mL/min/1.73 m2 compared with those with eGFR≥60 mL/min/1.73 m2. Prevalence of microalbuminuria was 22.76%. In total, 1076 out of 2721 (39.54%) patients were coded for microalbuminuria or proteinuria on their primary care medical record. Conclusions: The general practitioner prompt study is the largest UK primary care-based, technology-driven, randomized controlled trial to support intensive intervention in high-risk group of multiethnic individuals with type 2 diabetes and microalbuminuria. This paper provides contemporary estimates for prevalent cardiovascular disease and adherence to evidence-based cardiovascular risk factor targets at baseline in a population with type 2 diabetes and microalbuminuria. The main trial results, including cost-effectiveness data, will be submitted for publication in 2018. Trial Registration: International Standard Randomized Controlled Trial Number ISRCTN14918517; http://www.isrctn.com/ISRCTN14918517 (Archived by WebCite at http://www.webcitation.org/6zqm53wNA) Registered Report Identifier: RR1-10.2196/9588

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0240.004

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 head0.069
GPT teacher head0.460
Teacher spread0.391 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

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

Citations2
Published2018
Admission routes1
Has abstractyes

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