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Record W4397046918 · doi:10.1681/asn.20223311s1342a

Follow-Up Care of Critically Ill Patients With AKI

2022· article· en· W4397046918 on OpenAlexaffabout
Rachel Jeong, Alix Clarke, Matthew T. James, Robert R. Quinn, Pietro Ravani, Sean M. Bagshaw, Henry T. Stelfox, Neesh Pannu, Ngan N. Lam

Bibliographic record

VenueJournal of the American Society of Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal function and acid-base balance
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsCritically illIntensive care medicineMedicine

Abstract

fetched live from OpenAlex

Background: Acute kidney injury (AKI) occurs in more than half of critically ill patients in the intensive care unit and is associated with adverse outcomes. The 2012 Kidney Disease Improving Global Outcomes guideline recommends follow-up at 3 months post-discharge for assessment of kidney health. It remains unclear whether these recommendations are followed. Our objective was to determine processes of follow-up care for critically ill patients with AKI. Methods: We conducted a retrospective cohort study in Alberta, Canada, using linked healthcare databases within the Alberta Kidney Disease Network. We included critically ill adult patients with evidence of AKI (defined as ≥50% or ≥26.5 μmol/L serum creatinine increase from baseline) from 2005-2018. The primary outcome was an outpatient nephrology follow-up visit within 3 months of discharge. Secondary outcomes were an outpatient serum creatinine or urine protein measurement, and a follow-up visit by a family physician within 3 months of discharge. Results: There were 29,732 critically ill adult patients with AKI. The median age was 68 years, 39% were female, and the median estimated glomerular filtration rate was 72 mL/min/1.73 m2. The cumulative incidence of receiving nephrology follow-up within 3 months before dying or requiring maintenance kidney replacement therapy was 5%. At 3 months, 64% and 28% of patients had an outpatient creatinine and urine protein measurement, respectively, and 89% received follow-up by a family physician. Factors associated with nephrology follow-up were younger age, urban residence, lower baseline estimated glomerular filtration rate, higher baseline albuminuria, previous nephrology visit, shorter hospitalization stay, higher severity of AKI, receipt of acute dialysis, inpatient nephrology consultation, kidney biopsy, and worse kidney function at the time of discharge. Conclusions: Many critically ill patients with AKI do not receive the recommended follow-up care. Our findings illustrate a significant gap in the transition of care for critically ill patients with AKI. Further research is needed to determine if follow-up care is associated with improved patient outcomes. Funding: Private Foundation Support

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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.

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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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