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The International Quotidian Dialysis Registry: Annual report 2005

2005· article· en· W4232773212 on OpenAlexaffvenueabout
Gihad Nesrallah, Amit X. Garg, Louise Moist, Rita S. Suri, Robert M. Lindsay

Bibliographic record

VenueHemodialysis International · 2005
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsWestern UniversityHumber River Regional Hospital
Fundersnot available
KeywordsMedicineComorbidityCohortDialysisHemodialysisPopulationDemographyFamily medicineEmergency medicineEnvironmental healthSurgeryInternal medicine

Abstract

fetched live from OpenAlex

The International Quotidian Dialysis Registry was designed to collect data describing treatments, characteristics, and outcomes of patients treated with quotidian hemodialysis (HD) worldwide. In July 2004, North American centers were first invited to enroll patients. By March 1, 2005, a total of 70 nocturnal and 8 short-daily HD patients from three Canadian and two US centers were enrolled. As recruitment continues, projected enrollment for 2005 may exceed 200 patients from North America alone. Preliminary analyses indicate that the current registry cohort is younger (mean age, 49.5 +/- 1.6 years) and carries a lower burden of comorbidity than the overall North American HD population. The low event rate expected in this cohort underlines the need for a large sample size if an appropriately powered survival study is to be undertaken. Increasing recruitment in the United States by including HD centers owned or managed by large dialysis organizations, and beginning overseas collaborations to include Australia, New Zealand, Europe, and South America will be the primary areas of focus for 2005.

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)0.007
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.009
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.006

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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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

Citations3
Published2005
Admission routes3
Has abstractyes

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