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Record W2607897563 · doi:10.1161/str.44.suppl_1.awp343

Abstract WP343: Role of the Clinical Pharmacist in Reducing Decision-to-Needle tPa Times During Code Stroke in the Emergency Department

2013· article· en· W2607897563 on OpenAlexaff
Kevin Brandon, Amanda Kramer, Catherine Mulawka

Bibliographic record

VenueStroke · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsBrandon University
Fundersnot available
KeywordsMedicinePharmacistEmergency departmentClinical pharmacyStroke (engine)PharmacyEmergency medicineMedical emergencyNursing

Abstract

fetched live from OpenAlex

Background: Clinical observation revealed inconsistent ED decision-to-needle tPa times due to differing processes including pharmacist mixing in the central pharmacy, ED RN mixing at the bedside, and holding the tPa vial for stroke neurologist to mix upon arrival at the bedside Purpose: Implement a consistent process for tPa calculation and mixing Reduce decision-to-needle times, increasing the opportunity for successful patient outcome Method: Conduct retrospective chart review to establish baseline tPa decision-to-needle times in the ED Review current process of calculating, mixing, and administering tPa Identify areas for process streamlining and reducing potential for error by calculating and mixing tPa at the bedside Established goal of zero minute decision-to-needle time Clinical Pharmacist added to Code Stroke pager team in May 2010 Initial Clinical Pharmacist role consisted of dose calculation and verification with ED nurse at bedside Expanded Clinical Pharmacist role to include mixing tPa at beside in conjunction with nurse, while maintaining compliance with USP 797 Guidelines * Implement process of bedside conference between Clinical Pharmacist and ED RN to identify eligible candidates, allowing the Clinical Pharmacist to prepare tPa in preparation of anticipated administration Clinical Pharmacist’s role progressed to include immediate bedside medication education to patient and/or family, including verbal and written education materials Results: Addition of Clinical Pharmacist to Code Stroke team, with responsibility of calculating and mixing tPa at the bedside, has resulted in improved zero minute decision-to-needle times. 2010: zero minute decision-to-needle time not achieved 2011: zero minute decision-to-needle achieved in 60% of administrations 2012: (through July): zero minute decision-to-needle achieved in 78% of administrations Conclusion: Addition of a Clinical Pharmacist to the Code Stroke team resulted in a reduction of decision-to-needle tPa times in the Emergency Department Implications for Practice: Utilization of Clinical Pharmacist during Code Stroke to calculate and mix tPa reduces decision-to-needle time * http://www.emlab.com/s/services/USP_797.html

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)0.003
metaresearch head score (Gemma)0.029
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.026
GPT teacher head0.339
Teacher spread0.312 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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