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Record W2247785001 · doi:10.1159/000438871

Improving Care after Acute Kidney Injury: A Prospective Time Series Study

2015· article· en· W2247785001 on OpenAlexafffund
Samuel A. Silver, Ziv Harel, Andrea Harvey, Neill K. J. Adhikari, Andrew Slack, Rey Acedillo, Arsh K. Jain, Robert Richardson, Christopher T. Chan, Glenn M. Chertow, Chaim M. Bell, Ron Wald

Bibliographic record

Venue˜The œNephron journals/Nephron journals · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsInstitute of Health Services and Policy ResearchToronto General HospitalUniversity Health NetworkSt. Michael's HospitalWestern UniversitySunnybrook Health Science Centre
FundersInstitute for Clinical Evaluative SciencesAmerican Society of Nephrology
KeywordsMedicineNephrologyAcute kidney injuryKidney diseaseInternal medicineProspective cohort studyRenal replacement therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Acute kidney injury (AKI) complicates 15-20% of hospitalizations, and AKI survivors are at increased risk of chronic kidney disease and death. However, less than 20% of patients see a nephrologist within 3 months of discharge, even though a nephrologist visit within 90 days of discharge is associated with enhanced survival. To address this, we established an AKI Follow-Up Clinic and characterized the patterns of care delivered. METHODS: We conducted a prospective time series study. All hospitalized patients who developed Kidney Disease Improving Global Outcomes (KDIGO) stage 2 or 3 AKI were eligible. The pre-intervention period consisted of electronic reminders to the nephrology consults and cardiovascular surgery services to refer to the AKI Follow-Up Clinic. In the post-intervention period, eligible patients were automatically scheduled into the AKI Follow-Up Clinic at discharge. The primary outcome was the percentage of KDIGO stages 2-3 AKI survivors assessed by a nephrologist within 30 days of discharge. RESULTS: In the pre-intervention period, 8 of 46 patients (17%) were seen by a nephrologist within 30 days after discharge, and no additional patients were seen for 90 days. In the post-intervention period, 17 of 69 patients (25%) were seen by a nephrologist within 30 days after discharge (p = 0.36), with an additional 30 patients seen in 90 days (47 of 69, 68%, p < 0.001). The mean serum creatinine was 99 (SD 35) µmol/l prior to hospitalization and 133 (58) µmol/l at 3 months. Fifty-five of 79 patients (70%) received at least 1 medical intervention at their first AKI Follow-Up Clinic visit. CONCLUSIONS: An AKI Follow-Up Clinic with an automatic referral process increased the proportion of patients seen at 90 days, but not 30 days post discharge. Being seen in the AKI Follow-Up Clinic was associated with interventions in most patients. Future research is needed to evaluate the effect of the AKI Follow-Up Clinic on patient-centered outcomes, but physicians should be aware that AKI survivors may benefit from close outpatient follow-up and a multipronged approach to care similarly for other high-risk populations.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.341
Teacher spread0.317 · 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 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".

Quick stats

Citations39
Published2015
Admission routes2
Has abstractyes

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