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Record W2018074479 · doi:10.1155/2013/691454

A Pharmacist-Led Point-of-Care INR Clinic: Optimizing Care in a Family Health Team Setting

2013· article· en· W2018074479 on OpenAlexaffabout
Jennifer Rossiter, Gursharan S. Soor, Deanna Telner, Babak Aliarzadeh, Jennifer Lake

Bibliographic record

VenueInternational Journal of Family Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsOntario Stroke NetworkHeadwaters Health Care CentreEast Wellington Family Health TeamUniversity of Toronto
Fundersnot available
KeywordsPharmacistMedicineFamily medicinePoint of careHealth careNursingPharmacy

Abstract

fetched live from OpenAlex

Purpose. Monitoring patients' international normalized ratio (INR) within a family medicine setting can be challenging. Novel methods of doing this effectively and in a timely manner are important for patient care. The purpose of this study was to determine the effectiveness of a pharmacist-led point-of-care (POC) INR clinic. Methods. At a community-based academic Family Health Team in Toronto, Canada, charts of patients with atrial fibrillation managed by a pharmacist with usual care (bloodtesting at lab and pharmacist follow up of INR by phone) from February 2008 to April 2008 were compared with charts of patients attending a weekly POC INR clinic from February 2010 to April 2010. Time in therapeutic range (TTR) was measured for both groups. Results. 119 patient charts were reviewed and 114 had TTR calculated. After excluding patients with planned inconsistent Coumadin use (20), such as initiating Coumadin treatment or stopping for a surgical procedure, the mean TTR increased from 64.41% to 77.09% with the implementation of the POC clinic. This was a statistically significant difference of 12.68% (CI: 1.18, 24.18; P = 0.03). Conclusion. A pharmacist-led POC-INR clinic improves control of anticoagulation therapy in patients receiving warfarin and should be considered for implementation in other family medicine settings.

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)0.001
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 score0.405
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.149
GPT teacher head0.486
Teacher spread0.337 · 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

Citations22
Published2013
Admission routes2
Has abstractyes

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