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Record W2911904731 · doi:10.1111/hdi.12723

Early withdrawal and non‐withdrawal death in the months following hemodialysis initiation: A retrospective cohort analysis

2019· article· en· W2911904731 on OpenAlexvenueno aff
James B. Wetmore, Nicholas S. Roetker, David T. Gilbertson, Jiannong Liu

Bibliographic record

VenueHemodialysis International · 2019
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineHemodialysisRetrospective cohort studyCause of deathCohortProportional hazards modelSurvival analysisInternal medicineLogistic regressionComorbidityDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: Whether and how factors associated with elective hemodialysis withdrawal differ from those associated with non-withdrawal death soon after maintenance hemodialysis initiation have not been well studied. METHODS: A retrospective cohort analysis was performed using USRDS data from 2011 to 2014. Patients were randomly categorized 2:1 into training and validation samples. Elective withdrawal deaths were identified using the Death Notification form. Multinomial logistic regression was used to fit a prediction model for three outcome categories (withdrawal, non-withdrawal death, survival at 6 months) as a function of demographic, comorbidity, and functional status. FINDINGS: The training sample comprised 80,284 hemodialysis patients. Mean age was 71.7 ± 11.4 years, 44.9% were female, 72.9% were white, and 22.8% were black. Within 6 months, 19.1% died, of whom 2099 (2.6%) withdrew and 13,223 (16.5%) died of a non-withdrawal cause; 13.7% of all deaths were withdrawals. Baseline characteristics and event rates were similar among the 40,142 patients in the validation sample. The model was calibrated adequately and could discriminate moderately well between withdrawal and survival (area under ROC curve [AUC]: 0.77) and between non-withdrawal death and survival (AUC: 0.73). However, discrimination between withdrawal and non-withdrawal death was relatively low (AUC: 0.62). Older age and white, compared with non-white, race were each associated with greater odds of death, and these associations were stronger for withdrawal than for non-withdrawal death. DISCUSSION: Advanced age and white, as opposed to black, race were most strongly associated with early elective hemodialysis withdrawal compared with non-withdrawal death. However, it is difficult to differentiate between patients who will experience early withdrawal vs. non-withdrawal death, as many factors are similarly associated with both outcomes.

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)0.003
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.254
Teacher spread0.246 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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