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Record W2945696186 · doi:10.1007/s11096-019-00844-0

Creating an Interprofessional guideline to support patients receiving oral anticoagulation therapy: a Delphi exercise

2019· article· en· W2945696186 on OpenAlexaff
Nikki L. Damen, Bart J. F. van den Bemt, Kurt E. Hersberger, John Papastergiou, Filipa Alves da Costa, Silas Rydant, Naomi S. Wartenberg, Trudy Lobban, Isabelle Arnet, Sotiris Antoniou

Bibliographic record

VenueInternational Journal of Clinical Pharmacy · 2019
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePharmacyGuidelineDelphiDelphi methodPhysical therapyIntensive care medicineFamily medicine

Abstract

fetched live from OpenAlex

Background Oral anticoagulation therapy has proven beneficial impact on the prevention of thromboembolic events. However, the use of antocoagulatns also increases the risk of bleeds. To maximize the benefits and minimize the risks of the treatment, guidance on appropriate use of oral anticoagulants is essential. An international guideline describing relevant components and requirements for pharmaceutical care for patients receiving a therapy woth oral antocoagulants would increase the quality of care. However, recommendations on pharmaceutical care for patients on anticoagulation is lacking. Objective This study aims to develop an interprofessional guideline to support patients in their use of oral anticoagulation therapy. Method Two systematic literature searches were performed on existing guidelines on the management and interventions to improve-oral anticoagulant use, to generate possible recommendations. Subsequently, an international expert panel with 26 pharmacists with extensive experience in clinical and/or scientific work on anticoagulation from a total of 22 European and 4 non-European countries was constituted. With this (geographically well distributed) expert panel, a four-round internet-based Delphi technique was conducted to reach consensus on their relevance. Items were ranked on a 1-10 scale of agreement. A median agreement score of ≥ 7.5 was considered the threshold for consensus. Levels of importance were rated on a 1-3 scale. Setting A global network of 26 pharmacists specialized in oral antocoagulation therapy. Main outcome measure Development of inter-professional guideline. Results After the four Delphi rounds 18 guideline recommendations were formulated. Consensus of opinion was achieved for all recommendations (median agreement: 8.5-10.0), whereas mean levels of importance were between 1.1 and 2.0 (SD: 0.2-0.7). The following domains were rated as most important targets for improving the care around oral anticoagulation: 'INR-monitoring', 'Transfer of care between health care settings', 'Adherence to medication', 'Patient communication and engagement', and 'Medication reconciliation and medication review'. Conclusion The 18 recommendations included in this guideline provide the base for optimization of anticoagulation care across different countries/healthcare systems. Future work involves translating the guideline recommendations into clinical practice. Once implemented, the recommendations of the guideline will support health care providers with the pharmaceutical care for patients on, oral anticoagulation which will improve the effective and safe use of these medicines.

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)0.095
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.504

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.090
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0050.002
Scholarly communication0.0040.004
Open science0.0020.010
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.198
GPT teacher head0.545
Teacher spread0.347 · 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 designQualitative
Domainnot available
GenreMethods

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

Citations10
Published2019
Admission routes1
Has abstractyes

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