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Record W2313402865 · doi:10.2146/ajhp120433

Highlights of the Cleveland Clinic Pharmacy Practice Model Summit

2013· article· en· W2313402865 on OpenAlexaboutno aff
Scott J. Knoer, Robert J. Weber, David R. Witmer, David A. Zilz, Daniel M. Ashby, Steve Rough, James G. Stevenson, Paul W. Bush, Rowell Daniels, Sam Calabrese, David Chen

Bibliographic record

VenueAmerican Journal of Health-System Pharmacy · 2013
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsSummitPharmacyOfficerManagementClinical pharmacyMedicineLibrary sciencePolitical scienceFamily medicineLawComputer science

Abstract

fetched live from OpenAlex

In November 2010, the American Society of Health-System Pharmacists (ASHP) kicked off its Pharmacy Practice Model Initiative (PPMI) with the PPMI Summit in Dallas, Texas. The PPMI is intended to provide direction for pharmacy leaders to advance the practice of health-system pharmacy within their institutions. The consensus recommendations of the PPMI Summit were published in the American Journal of Health-System Pharmacy and can be found on the ASHP website.1 The PPMI is timely for the profession, as it defines the pharmacist’s role in dealing with the challenges of health care reform, rising prescription drug costs, new technology, and complex drug therapy regimens. The pharmacy leadership at the Cleveland Clinic was inspired by the PPMI Summit and wanted to spread the inspiration to the more than 800 pharmacy employees of the institution’s integrated health system, which comprises 10 hospitals and 17 family health and ambulatory surgical centers, primarily located in northeastern Ohio, and other facilities in Weston, Florida; Las Vegas, Nevada; Toronto, Ontario, Canada; and Abu Dhabi, United Arab Emirates.

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)0.033
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.033
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.038
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0150.004
Scholarly communication0.0130.006
Open science0.0050.015
Research integrity0.0190.029
Insufficient payload (model declined to judge)0.0200.003

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 head0.042
GPT teacher head0.386
Teacher spread0.344 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2013
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Health-System PharmacySame topicHospital Admissions and OutcomesFrench-language works237,207