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Record W2911132248 · doi:10.1016/j.ijsu.2019.01.006

Clinical pharmacist perspectives for optimizing pharmacotherapy within Enhanced Recovery After Surgery (ERAS®) programs

2019· review· en· W2911132248 on OpenAlexaff
Jenna K. Lovely, Sara J. Hyland, April N. Smith, Gregg Nelson, Olle Ljungqvist, Richard H. Parrish

Bibliographic record

VenueInternational Journal of Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPharmacotherapyMedicinePerioperativePharmacistClinical pharmacyPharmacyIntensive care medicinePharmacy practiceNursingSurgery

Abstract

fetched live from OpenAlex

One of the most durable approaches to perioperative enhanced recovery programming has culminated in the formation of perioperative organizations devoted to improvements in the quality of the surgical patient experience, such as the Enhanced Recovery After Surgery (ERAS®) Society. Members of the American College of Clinical Pharmacy (ACCP) Perioperative Care Practice and Research Network (PRN) and officials from the ERAS® Society present an opinion that: (1) identifies therapeutic options within each pharmacotherapy-intensive area of ERAS®; (2) generates applied research questions that would allow for comparative analyses of pharmacotherapy options within ERAS® programs; (3) proposes collaborative practice opportunities between key stakeholders in the surgical journey and clinical pharmacists to manage drug therapy problems and research questions; and (4) highlights examples of pharmacist-led cost savings attributed to ERAS® implementation. Clinical pharmacists, working in this manner with the perioperative team across the care continuum, have optimized pharmacotherapy towards measurable outcomes improvements, and stand ready to partner with inter-professional stakeholders and organizations to advance the care of our mutual patients.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.009
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.194
GPT teacher head0.462
Teacher spread0.267 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations33
Published2019
Admission routes1
Has abstractyes

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