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Record W2029593178 · doi:10.1159/000185197

Dialyzer Re-Use – A Multiple Crossover Study with Random Allocation to Order of Treatment

2008· article· en· W2029593178 on OpenAlexaff
David Churchill, D. Wayne Taylor, Arthur Shimizu, Mary Louise Beecroft, Jack W. Singer, C.C. Barnes, D Ludwin, N. Wright, D L Sackett, E.K.M. Smith

Bibliographic record

Venue˜The œNephron journals/Nephron journals · 2008
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
Fundersnot available
KeywordsWashoutMedicineCrossover studyNauseaDialysisCreatinineBlood urea nitrogenAnesthesiaCrossoverHemodialysisSurgeryInternal medicine

Abstract

fetched live from OpenAlex

A multiple crossover research study was used to evaluate the effect of dialyzer re-use on fever, blood leaks, serum urea and creatinine values and symptoms. Each of 6 crossover periods consisted of 4 weeks on either single-use or re-use, 1 week washout, 4 weeks on the alternative treatment and 1 week washout. The re-use consisted of 6 uses of each dialyzer and the washout weeks consisted of 3 single-use sessions. Analysis of paired observations within rather than between patients showed no effects of time (i.e. among crossover periods 1 through 6) or number of re-uses (i.e. among uses 1 through 6). There was no significant difference for temperature change during dialysis, blood leak rate, or the serum urea and creatinine values before the first dialysis of each washout period. There were no differences for symptoms of pruritus, cramps, nausea, headache, chest pain, backache or fatigue. There were no clinical advantages or disadvantages associated with dialyzer re-use.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.308
Teacher spread0.264 · 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.

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
Published2008
Admission routes1
Has abstractyes

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