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Record W2312911191 · doi:10.1097/mcc.0000000000000261

Improving outcomes of acute kidney injury survivors

2015· review· en· W2312911191 on OpenAlexaffabout
Samuel A. Silver, Ron Wald

Bibliographic record

VenueCurrent Opinion in Critical Care · 2015
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineAcute kidney injuryIntensive care medicineExacerbationNephrologyKidney diseaseDialysisAmbulatory careHealth careEmergency medicinePopulationDiseasePublic healthInternal medicineNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Acute kidney injury (AKI) is a common problem in critically ill patients, with long-term health implications that extend beyond hospital discharge. Though they are at a high risk of adverse events, AKI survivors may not be receiving adequate postdischarge medical attention. This review discusses recently published data regarding health outcomes after AKI, the current state of post-AKI care, and potential opportunities to improve outpatient care after AKI. RECENT FINDINGS: In addition to predisposing to de-novo chronic kidney disease or an exacerbation of previously existing chronic kidney disease, a prior episode of AKI has been linked to subsequent cardiac events, cerebrovascular events, and the need for hospital readmission. Despite this, a population-wide study in Ontario showed that only 40% of patients surviving an episode of dialysis-requiring AKI visited a nephrologist within 90 days of hospital discharge. This care gap is important since outpatient contact with a nephrologist during this critical period was associated with enhanced survival. SUMMARY: AKI is associated with a number of long-term health effects, and new strategies may be needed to address this emerging public health issue. An ambulatory program dedicated to the postdischarge care of AKI survivors may confer a variety of benefits. Future research is needed to evaluate this model of care.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.280
GPT teacher head0.553
Teacher spread0.274 · 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 designNot applicable
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

Citations15
Published2015
Admission routes2
Has abstractyes

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