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Record W2183438123 · doi:10.1111/hdi.2000.4.1.55

What Went Wrong with Home Hemodialysis in the United States and What Can Be Done Now?

2000· article· en· W2183438123 on OpenAlexvenueno aff
Christopher R. Blagg

Bibliographic record

VenueHemodialysis International · 2000
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisDialysisHome hemodialysisIntensive care medicinePeritoneal dialysisContinuous ambulatory peritoneal dialysisHome dialysisSurgery

Abstract

fetched live from OpenAlex

In 1973, almost 40% of the more than 10 000 dialysis patients were treated by home hemodialysis. Today, with more than a quarter of a million dialysis patients in the United States, fewer than 2000 are on home hemodialysis. A number of factors have contributed to this change. First, many nephrologists and administrators who were developing new dialysis units had little or no practical experience with dialysis for chronic renal failure. Second, more elderly and diabetic patients were admitted to treatment. Home hemodialysis was more difficult for such patients, and often their helpers were themselves were elderly. Third, hemodialysis machines were difficult to learn and operate. Fourth, following publication of the results of the National Cooperative Dialysis Study, there developed the erroneous concept that a Kt/V equal to 1.0 was "adequate dialysis." As bigger dialyzers became available, there was a widespread shortening of dialysis time. This decrease in time was embraced by for-profit dialysis facilities and inadequately educated patients, and assembly-line dialysis became generally accepted. Finally, continuous ambulatory peritoneal dialysis, with its simplicity and short training time, began to fill the need of many patients for home dialysis and independence, at least temporarily. Fortunately, the trend is now reversing. Two developments clearly have benefits for home hemodialysis. The first is an increasing interest in the use of more frequent dialysis. The second is the development of new equipment designed specifically for use by the patient, and requiring a minimum of effort on the patient's part.

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)0.010
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.010
Scholarly communication0.0080.012
Open science0.0020.002
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.250
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations27
Published2000
Admission routes1
Has abstractyes

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