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Record W2592809239 · doi:10.4037/aacnacc2017122

Training and Maintaining: Developing a Successful and Dynamic Continuous Renal Replacement Therapy Program

2017· article· en· W2592809239 on OpenAlexaboutno aff
Heather Przybyl, Jill Evans, Laurie Haley, Jodi Bisek, Emily Beck

Bibliographic record

VenueAACN Advanced Critical Care · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsRenal replacement therapyMedicineIntensive care medicineCritically illFidelityTraining (meteorology)HemodialysisKidney diseaseAcute kidney injuryNursingMedical emergencyPhysical therapySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Continuous renal replacement therapy (CRRT) is commonly used to support critically ill patients with acute kidney injury or chronic renal disease whose condition is too unstable for them to tolerate intermittent hemodialysis. Current publications related to CRRT programs in the United States and Canada note key themes related to the development and maintenance of CRRT training programs. A successful CRRT training program should consider and incorporate adult learning principles whenever possible. A variety of teaching methods to deliver information to nurses, including online learning modules, didactic lecture, return demonstration, and high-fidelity patient simulation are key to training programs for this high-risk complex therapy. This article outlines the approach to training nurses to care for patients receiving CRRT at a health care system in Arizona.

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)0.005
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.437
Teacher spread0.382 · 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
GenreMethods

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

Citations18
Published2017
Admission routes1
Has abstractyes

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