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Record W1944269200 · doi:10.1111/hdi.12036

Determinants of training and technique failure in home hemodialysis

2013· article· en· W1944269200 on OpenAlexaffvenue
Michael Schächter, Karthik Tennankore, Christopher T. Chan

Bibliographic record

VenueHemodialysis International · 2013
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineHemodialysisOdds ratioDialysisHome hemodialysisLogistic regressionConfidence intervalPopulationInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Home hemodialysis (HHD) has clinical and economic advantages compared with in-center conventional hemodialysis. Many health systems wish to broaden the population to which this modality can be successfully offered. However, determinants of successful HHD training and technique survival are unknown. We hypothesize that both medical and social factors play a role when patients fail to successfully adopt HHD. We examined characteristics of consecutive patients who initiated training for HHD between 2003 and 2011. Patients were classified as "failure" if they failed to complete HHD training or experienced technique failure (TF) within the first year of treatment. Remaining patients were classified as "success." One hundred seventy-seven patients initiated HHD training. In the "failure" group (n = 32), 24 did not finish training and 8 had TF. In the "success" group (n = 145), 65 (45%) patients remained on NHD, 49 (34%) discontinued HHD because of renal transplantation and 21 (14%) because of death, while only 10 (7%) eventually transferred to another dialysis modality. In a multivariable logistic regression analysis, the strongest predictors of "failure" were end-stage renal disease because of diabetes (odds ratio [OR] 3.8, 95% confidence interval [CI] 1.4-10.3, P = 0.008) and use of rental housing (OR 3.1, 95% CI 1.3-6.0, P = 0.01). Both medical and social factors are associated with failure to adopt HHD. Enhanced supports or a customized education strategy for these vulnerable patients should be considered.

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 score0.202
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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".

Quick stats

Citations43
Published2013
Admission routes2
Has abstractyes

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