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Record W4414161725 · doi:10.2215/cjn.0000000856

Association of Individual- and Neighborhood-Level Social Determinants of Health with Post-Hospitalization Acute Kidney Injury Care

2025· article· en· W4414161725 on OpenAlexaff
Tomonori Takeuchi, Seda Babroudi, Lama Ghazi, Elizabeth Baker, Gabriela R. Oates, Lucia Juarez, Ariann Nassel, Samuel A. Silver, Orlando M. Gutiérrez, Javier A. Neyra

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsQueen's University
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsAcute kidney injuryIncidence (geometry)DialysisIntensive care unitRetrospective cohort studySocial determinants of healthKidney diseaseCohortLogistic regression

Abstract

fetched live from OpenAlex

Key Points Only one third of intensive care unit patients who developed severe AKI received Kidney Disease Improving Global Outcomes–recommended follow-up care within 3 months of AKI. Both individual-level and neighborhood-level socioeconomic disadvantages are associated with decreased post-AKI follow-up care. Background Individual-level and neighborhood-level social determinants of health (SDOH) measures have been associated with higher incidence of AKI, lower likelihood of recovery, and higher risk of mortality after AKI. The association of SDOH measures with posthospitalization AKI follow-up care is unknown. Methods Using a retrospective cohort design, we evaluated the association of individual-level (insurance status, race, ethnicity) and neighborhood-level (socioeconomic deprivation, rurality, residential segregation, and social vulnerability to natural or human-caused disasters) SDOH measures with receipt of posthospitalization follow-up for AKI within 3 months of hospital discharge among intensive care unit (ICU) survivors with AKI stage 2 or 3 hospitalized between 2012 and 2023 at a major academic medical center. The primary outcome, posthospitalization AKI follow-up, was defined as the occurrence of at least one of the following within 3 months of hospital discharge: a nephrology outpatient visit, serum creatinine measurement, or urine protein measurement. We used pooled logistic regression models with inverse probability of censoring weighting to adjust for demographics, comorbidities, and hospitalization characteristics and to account for the competing risks of death, rehospitalization, or dialysis initiation. Results Among 13,392 adult ICU survivors with AKI stages 2 or 3, 5970 (45%) were female, 4488 (34%) were of Black race, and 1561 (12%) were uninsured. A total of 7316 (61%) received posthospitalization follow-up for AKI within 3 months of hospital discharge. Uninsured individuals (adjusted odds ratio [aOR], 0.77; 95% confidence interval [CI], 0.70 to 0.84), individuals residing in a neighborhood with greater socioeconomic deprivation (aOR, 0.86; 95% CI, 0.81 to 0.92), greater rurality (aOR, 0.86; 95% CI, 0.81 to 0.92), greater segregation (aOR, 0.92; 95% CI, 0.87 to 0.98), and greater social vulnerability (aOR, 0.83; 95% CI, 0.77 to 0.89) all experienced significantly lower odds of posthospitalization AKI care. Conclusions Both individual-level and neighborhood-level SDOH were associated with lower odds of post-AKI follow-up among ICU survivors with severe AKI.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.383
Teacher spread0.342 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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