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Record W2208351268 · doi:10.1159/000437152

Acute Kidney Injury Care Bundles

2015· article· en· W2208351268 on OpenAlexafffund
Sean M. Bagshaw

Bibliographic record

Venue˜The œNephron journals/Nephron journals · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Alberta
FundersCanada Research Chairs
KeywordsMedicineIntensive care medicineContext (archaeology)Acute kidney injuryHealth carePopulationCritical care nursingAcute careMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

Acute kidney injury (AKI) is a common complication that occurs in hospitalized patients and appears susceptible to a wide variability in practice. This may lead to suboptimal quality of care. The concept of a 'care bundle' for AKI has been proposed to improve the reliability and quality of care. A bundle is designed to be a structured method of improving care processes and outcomes. It contains a small set of evidence-based practices intended for a defined population and care setting. The Institute for Healthcare Improvement has developed guidelines for the design of care bundles. Care bundles for critically ill patients focusing on mechanical ventilation, central venous catheters, and sepsis have been widely implemented with modest success in terms of compliance and impact on care processes and outcomes. A care bundle for AKI is highly desired, given the observed practice variation and indication of poor care for AKI patients; however, existing proposals are too comprehensive and have not been focused on a defined population at-risk, clinical context or setting. They have also not engaged local stakeholders in the process.

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), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0070.002

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 head0.062
GPT teacher head0.384
Teacher spread0.322 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations40
Published2015
Admission routes2
Has abstractyes

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