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Record W3102626203 · doi:10.1016/j.diabres.2020.108535

Adoption of the ADA/EASD guidelines in 10 Eastern and Southern European countries: Physician survey and good clinical practice recommendations from an international expert panel

2020· article· en· W3102626203 on OpenAlexfundno aff
Maciej Banach, Dan Gaiță, Martin Haluzı́k, Andrej Janež, Zdravko Kamenov, Péter Kempler, Nebojša Lalić, Aleš Linhart, Dimitri P. Mikhailidis, Aleksandra Nocoń, José Silva‐Nunes, Νικόλαος Παπάνας, João Filipe Raposo, Manfredi Rizzo, Anca Pantea Stoian

Bibliographic record

VenueDiabetes Research and Clinical Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
FundersJanssen PharmaceuticalsNovo Nordisk PharmaEsperion TherapeuticsSanofiWörwag PharmaMylanNovo NordiskValeant Pharmaceuticals InternationalServierAmgenPfizerAstraZenecaEli Lilly and Company
KeywordsMedicineReimbursementFamily medicineMEDLINEHealth care

Abstract

fetched live from OpenAlex

AIMS: Evidence from cardiovascular outcomes trials (CVOTs) of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors was reflected in the most recent guidelines from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). The aim of the present study was to assess the adoption of the ADA/EASD guidelines in a convenience sample of physicians from Eastern and Southern Europe, the barriers to the implementation of these guidelines and the measures needed to facilitate their implementation. METHODS: Attendees at two international diabetes conferences could volunteer to respond to a fully anonymous survey. Responses were analysed descriptively and a panel of experts from around the region was consulted to interpret the survey results. RESULTS: Responses (n = 96) from 10 countries were analysed. Most participants (63.4%) considered the ADA/EASD guidelines fundamental to their practice. All respondents saw the value of the CVOT-based ADA/EASD recommendations and 77-80% generally implemented them. Measures suggested to improve adherence to the ADA/EASD guidelines included aligning reimbursement policy with the guidelines (54.4%), publishing guidelines in a simple and concise form (42.4%) and translating guidelines into local languages (33.3%). CONCLUSIONS: Aligning reimbursement with recent evidence and providing short summaries of the ADA/EASD guidelines in local languages could facilitate physician adherence.

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)0.012
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.309
GPT teacher head0.513
Teacher spread0.204 · 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 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

Citations24
Published2020
Admission routes1
Has abstractyes

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