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Record W1979292598 · doi:10.12927/hcq.2009.21101

Enhancing Patient Care via a Pharmacist-Managed Rural Anticoagulation Clinic

2009· article· en· W1979292598 on OpenAlexaff
Cindy Jones, Guy Lacombe

Bibliographic record

VenueHealthcare Quarterly · 2009
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsAthabasca University
Fundersnot available
KeywordsMedicinePharmacistTelehealthPharmacyMedical emergencyPatient educationMedication therapy managementWarfarinNursingIntensive care medicineHealth careTelemedicine

Abstract

fetched live from OpenAlex

Integrating specialized pharmacist services and follow-up with the laboratory, home care nursing, retail pharmacy and physicians can ensure optimal outcomes for patients receiving anticoagulation, or "blood thinner," therapy. Improved patient education and discharge care planning can bridge disconnects, enable patients to better manage their care and ensure better patient outcomes and more effective use of health system resources. Specially trained pharmacists can provide safe and effective management of a high-alert medication to help prevent potentially life-threatening clots or bleeding. With advanced prescribing authorization, the pharmacist can seamlessly provide this service both locally in a community and via Telehealth to surrounding areas, potentially for any Albertan. Warfarin therapy may be lifelong or short-term (three to six months), but all patients require regular monitoring with blood tests. Many variables, both lifestyle and medication related, can impact therapy, and through extensive education and access via telephone to an "expert" for questions and follow-up of blood tests, patients are empowered to better regulate their anticoagulants. Anticoagulation pharmacists, as part of an AMS (anticoagulation management service), can provide a continuum of care for patients while in hospital, when discharged home, as an outpatient in the community or as a resident of a long-term care facility or seniors' home.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.052
GPT teacher head0.383
Teacher spread0.331 · 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

Citations10
Published2009
Admission routes1
Has abstractyes

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