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Record W1593706834 · doi:10.1159/000071427

Cardiovascular Risk in Peritoneal Dialysis

2003· review· en· W1593706834 on OpenAlex
Sarah Prichard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueContributions to nephrology · 2003
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsMcGill University Health CentreRoyal Victoria Regional Health CentreRoyal Victoria Hospital
Fundersnot available
KeywordsMedicinePeritoneal dialysisDiabetes mellitusHyperinsulinemiaDialysisIntensive care medicineRisk factorInternal medicineAnemiaTransplantationEndocrinologyInsulin resistance

Abstract

fetched live from OpenAlex

All patients with CKD have multiple risk factors for CVD and CAD in particular. Some of these risk factors such as age and gender cannot be modified. Others such as diabetes and hypertension are not only CVD risk factors but are also the cause of the patient's CKD. Finally there are a group of risk factors such as disturbances of mineral metabolism and oxidative stress which are present either uniquely in or are exaggerated by renal failure. PD gives patients a more atherogenic lipid and lipoprotein profile, puts them at greater risk for AGE formation and usually causes hyperinsulinemia. All of these contribute to CVD risk. However, they can also achieve excellent blood pressure control, usually easily reach targets for anemia management and have continuous ultrafiltration allowing for the maintenance of good volume status, all of which will reduce risk for CVD. All treatable risk factors should be treated early in the development of CKD and should continue through their time on dialysis and after transplantation.

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.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.017
GPT teacher head0.318
Teacher spread0.300 · 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